Mathematical Problems Dataset: Various Mathematical Problems and Solutions

Mathematical Problems Dataset: Questions and Answers

By math_dataset (From Huggingface) [source]

About this dataset

This dataset comprises a collection of mathematical problems and their solutions designed for training and testing purposes. Each problem is presented in the form of a question, followed by its corresponding answer. The dataset covers various mathematical topics such as arithmetic, polynomials, and prime numbers. For instance, the arithmetic_nearest_integer_root_test.csv file focuses on problems involving finding the nearest integer root of a given number. Similarly, the polynomials_simplify_power_test.csv file deals with problems related to simplifying polynomials with powers. Additionally, the dataset includes the numbers_is_prime_train.csv file containing math problems that require determining whether a specific number is prime or not. The questions and answers are provided in text format to facilitate analysis and experimentation with mathematical problem-solving algorithms or models

How to use the dataset

• Introduction: The Mathematical Problems Dataset contains a collection of various mathematical problems and their corresponding solutions or answers. This guide will provide you with all the necessary information on how to utilize this dataset effectively.

• Understanding the columns: The dataset consists of several columns, each representing a different aspect of the mathematical problem and its solution. The key columns are:

  • question: This column contains the text representation of the mathematical problem or equation.
  • answer: This column contains the text representation of the solution or answer to the corresponding problem.

• Exploring specific problem categories: To focus on specific types of mathematical problems, you can filter or search within the dataset using relevant keywords or terms related to your area of interest. For example, if you are interested in prime numbers, you can search for prime in the question column.

• Applying machine learning techniques: This dataset can be used for training machine learning models related to natural language understanding and mathematics. You can explore various techniques such as text classification, sentiment analysis, or even sequence-to-sequence models for solving mathematical problems based on their textual representations.

• Generating new questions and solutions: By analyzing patterns in this dataset, you can generate new questions and solutions programmatically using techniques like data augmentation or rule-based methods.

• Validation and evaluation: As with any other machine learning task, it is essential to validate your models on separate validation sets not included in this dataset properly. You can also evaluate model performance by comparing predictions against known answers provided in this dataset's answer column.

• Sharing insights and findings: After working with this datasets, it would be beneficial for researchers or educators to share their insights, approaches taken during analysis/modelling as Kaggle notebooks/ discussions/ blogs/ tutorials etc., so that others could get benefited from such shared resources too.

Note: Please note that the dataset does not include dates.

By following these guidelines, you can effectively explore and utilize the Mathematical Problems Dataset for various mathematical problem-solving tasks. Happy exploring!

Research Ideas

• Developing machine learning algorithms for solving mathematical problems: This dataset can be used to train and test models that can accurately predict the solution or answer to different mathematical problems.
• Creating educational resources: The dataset can be used to create a wide variety of educational materials such as problem sets, worksheets, and quizzes for students studying mathematics.
• Research in mathematical problem-solving strategies: Researchers and educators can analyze the dataset to identify common patterns or strategies employed in solving different types of mathematical problems. This analysis can help improve teaching methodologies and develop effective problem-solving techniques

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purpos...

Abstract

The Trends in International Mathematics and Science Study (TIMSS) is an international assessment of the mathematics and science knowledge of 9–10 and 13–14 year old (Year 5 and Year 9 or fourth grade and eighth grade) students around the world. TIMSS was developed by the International Association for the Evaluation of Educational Achievement (IEA) to allow participating nations to compare students' educational achievement across borders. TIMSS was first administered in 1995, and every 4 years thereafter. In 1995, forty-one nations participated in the study. TIMSS consists of an assessment of mathematics and science, as well as student, teacher, and school questionnaires. The current assessment includes those topics in mathematics and science that students are likely to have been exposed to up to and including grade 4 and grade 8.

Geographic coverage

The survey had international coverage

Analysis unit

Units of analysis in the study include documents, schools and individuals

Universe

The study covered curricula and text-books, teachers and pupils at selected schools in the country

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

Data for the study was gathered through assessments of curricular documentation, and with questionnaires, including student, teacher (mathematics and science teachers), and school background questionnaires. Data Almanac files from the survey contain weighted summary statistics for each participating country on each variable in each of the questionnaires.

Public and Catholic board-level course enrolment in Grade 9 Math by course type (academic, applied and locally developed) for each academic year. School boards report this data using the Ontario School Information System (OnSIS). Includes: * academic year * board number * board name * course grade * course type * number of students * percentage of students Data excludes private schools, school authorities, publicly funded hospital and provincial schools, Education and Community Partnership Program (ECPP) facilities, summer, night and adult continuing education day schools. Enrolment totals include withdrawn and dropped courses. Students enrolled in more than one course are counted for each course. Cells are suppressed in categories with less than 10 students. Enrolment totals are rounded to the nearest five.

The thesis project focuses on "Personal Relevance and Interest Development in the Secondary School Math Class". The current dataset includes the data collected in Study 2, 3 and 4 in the thesis project. Study 2 developed and validated a measure assessing three types of personal relevance with varying degree of personal meaningfulness (personal association, personal usefulness, relevance as identification. The dataset for this study includes students’ three types of personal relevance collected at three time points using the newly-developed questionnaire (434 first-year middle school students). Study 3 investigated the relationships among the three types of personal relevance and students’ interest in middle school math classes. The dataset for Study 3 includes the participants’ self-reported three types of personal relevance, self-efficacy for math class, class interest and teacher-reported math grades (434 first-year middle school students). Study 4 implemented an intervention utilizing generative AI to foster students’ relevance as identification with the knowledge/skills taught in the math class and examined the impact of the intervention on promoting students’ interest in the math class. The dataset for Study 4 includes the participants’ self-reported relevance as identification with math, class interest, self-efficacy for math and teacher-reported math grades (218 high school students).

Abstract

TIMSS measures trends in mathematics and science achievement at the fourth and eighth grades in participating countries around the world, as well as monitoring curricular implementation and identifying promising instructional practices. Conducted on a regular 4-year cycle, TIMSS has assessed mathematics and science in 1995, 1999, 2003, and 2007, with planning underway for 2011. TIMSS collects a rich array of background information to provide comparative perspectives on trends in achievement in the context of different educational systems, school organizational approaches, and instructional practices. To support and promote secondary analyses aimed at improving mathematics and science education at the fourth and eighth grades, the TIMSS 2007 international database makes available to researchers, analysts, and other users the data collected and processed by the TIMSS project. This database comprises student achievement data as well as student, teacher, school, and curricular background data for 59 countries and 8 benchmarking participants. Across both grades, the database includes data from 433,785 students, 46,770 teachers, 14,753 school principals, and the National Research Coordinators of each country. All participating countries gave the IEA permission to release their national data.

Geographic coverage

The survey had national coverage

Analysis unit

Units of analysis in the study include documents, schools and individuals

Universe

The TIMSS target populations are all fourth and eighth graders in each participating country. The teachers in the TIMSS 2007 international database do not constitute representative samples of teachers in the participating countries. Rather, they are the teachers of nationally representative samples of students. Therefore, analyses with teacher data should be made with students as the units of analysis and reported in terms of students who are taught by teachers with a particular attribute. Teacher data are analyzed by linking the students to their teachers. The student-teacher linkage data files are used for this purpose.

Kind of data

Sample survey data [ssd]

Sampling procedure

The TIMSS target populations are all fourth and eighth graders in each participating country. To obtain accurate and representative samples, TIMSS used a two-stage sampling procedure whereby a random sample of schools is selected at the first stage and one or two intact fourth or eighth grade classes are sampled at the second stage. This is a very effective and efficient sampling approach, but the resulting student sample has a complex structure that must be taken into consideration when analyzing the data. In particular, sampling weights need to be applied and a re-sampling technique such as the jackknife employed to estimate sampling variances correctly.

In addition, TIMSS 2007 uses Item Response Theory (IRT) scaling to summarize student achievement on the assessment and to provide accurate measures of trends from previous assessments. The TIMSS IRT scaling approach used multiple imputation-or "plausible values"-methodology to obtain proficiency scores in mathematics and science for all students. Each student record in the TIMSS 2007 international database contains imputed scores in mathematics and science overall, as well as for each of the content domain subscales and cognitive domain subscales. Because each imputed score is a prediction based on limited information, it almost certainly includes some small amount of error. To allow analysts to incorporate this error into analyses of the TIMSS achievement data, the TIMSS database provides five separate imputed scores for each scale. Each analysis should be replicated five times, using a different plausible value each time, and the results combined into a single result that includes information on standard errors that incorporate both sampling and imputation error.

Mode of data collection

Face-to-face [f2f]

Research instrument

The study used the following questionnaires: Fourth Grade Student Questionnaire, Fourth Grade Teacher Questionnaire, Fourth Grade School Questionnaire, Eighth Grade Student Questionnaire, Eighth Grade Mathematics Teacher Questionnaire, Eighth Grade Science Teacher Questionnaire, and Eighth Grade School Questionnaire. Information on the variables obtained or derived from questions in the survey is available in the TIMSS 2007 user guide for the international database: Data Supplement3: Variables derived from the Student, Teacher, and School Questionnaire data.

The data include information about 85 rigorous experimental studies that evaluated 64 programs in grades K-5 mathematics. These data were collected by the research team from studies included in a systematic review of programs for elementary mathematics. The data contain study and finding level information to examine what types of programs are most effective.

"Mathematical dataset based on 71 famous mathematical identities. Each entry consists of a name of the identity (name), a representation of that identity (formula), a label whether the representation belongs to the identity (label), and an id of the mathematical identity (formula_name_id). The false pairs are intentionally challenging, e.g., a^2+2^b=c^2as falsified version of the Pythagorean Theorem. All entries have been generated by using data.json as starting point and applying the randomizing and falsifying algorithms here. The formulas in the dataset are not just pure mathematical, but contain also textual descriptions of the mathematical identity. At most 400000 versions are generated per identity. There are ten times more falsified versions than true ones, such that the dataset can be used for a training with changing false examples every epoch."

https://huggingface.co/datasets/ddrg/named_math_formulas

The Rwanda Quality Basic Education for Human Capital Development (RQBEHCD) is a World Bank Group financed project through the government of Rwanda to support Mathematics and Science teachers from upper primary and lower secondary schools. The project was confirmed in 2019 and initiated in 2020. The dataset deposited here comprises two types of data; (1) teacher performance scores per subject taught [Math (for both primary and secondary school teachers), Physics, Chemistry, Biology taught in secondary, and Science and Elementary Technology (SET) taught in upper primary school], (2) teacher belief scores. The data were collected before and after a continuous profession development (CPD) training program of five months starting from March to July 2023. The training program comprised four channels that are ICT integration in teaching math and science, content knowledge (SCK), Math and Science laboratory activities, and innovative pedagogy. The data are collected from seven districts of Rwanda that were involved in the second cohort of training (2022-2023).

The Mathematics Enrichment Classes market has seen significant evolution over recent years, driven by a growing emphasis on STEM (Science, Technology, Engineering, and Mathematics) education across the globe. These classes provide students with advanced mathematical skills, catering not only to those who seek to enh

The current study is dedicated to obtaining a more thorough understanding of the use of variation and connections in naturalistic mathematics teaching practices in China. The research object is the mathematics topic of functions in the senior secondary school curriculum, which requires approximately 8–16 lessons to fit the specific situations of different classes. The participants were six ordinary mathematics teachers in three locally renowned schools located in three different cities in China. Various data collection methods were applied in this research to identify more information on natural real-world teaching design regarding the use of variation and connections. First, observation with video recording was conducted in all lessons to capture more details and can be repeatedly viewed and examined. The essential information has been extracted and integrated, which can be found in the file "Video Note". Second, semi-structured interviews were conducted with teachers to gather their basic information, explore their intentions and reflections about lessons, and validate the ideas of the researcher. This information can be found in the file "Interview". Third, students' performances were also collected from tests, which can be found in the file "Test". The data of all types were categorized by teachers, i.e., the video set of lessons taught by each teacher, the interview of each teacher, and the overall test results of the class taught by each teacher. Therefore, there are usually six cases corresponding to six teachers in all files.

The number of pupils making 2 levels progress between Key Stages where prior attainment data exists against the number of eligible pupils in cohort with matched valid results at KS1, expressed as a percentage. Source: Department for Children Schools and Families (DCSF) Publisher: DCLG Floor Targets Interactive Geographies: County/Unitary Authority, Government Office Region (GOR), National Geographic coverage: England Time coverage: 2003/04 to 2007/08 Type of data: Survey (census)

Abstract

This study examines the effect of the use of two Open Educational Resources (OER) (a Khan Academy online tutorial and an open textbook hosted on Wikibooks) on logical-mathematical outcomes for first and second-year students in higher education institutions in Chile. It also investigates perceptions of instructors and students about the use of OER, in order to understand how these resources are used and valued. Quantitative and qualitative methods were used to collect student performance data via a student survey, student focus groups, interviews with instructors, and sourcing institutional records.

Only the institutional records, focus group data and interview data are included in the final dataset. Student survey data is not made available for confidentiality reasons. Findings indicate that students in a contact-study mathematics course who used a Khan Academy online mathematics tutorial obtained better examination results than students who did not use any additional resources, or those who used the open textbook. Moreover, it was also found that instructors and students have positive perceptions about the use of Khan Academy and Wikibooks materials.This study is Sub-project 9 of the Research on Open Educational Resources for Development (ROER4D) project, hosted by the Centre for Innovation in Learning and Teaching (CILT) at the University of Cape Town, South Africa, and Wawasan Open University, Malaysia.

Geographic coverage

The interviews and survey data were conducted at one institution in Chile and are not representative of the country as a whole.

Analysis unit

Individuals

Universe

The survey covered students and instructors in the single institution involved in the study.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face and internet [f2f-int]

Mathematics database.

This dataset code generates mathematical question and answer pairs, from a range of question types at roughly school-level difficulty. This is designed to test the mathematical learning and algebraic reasoning skills of learning models.

Original paper: Analysing Mathematical Reasoning Abilities of Neural Models (Saxton, Grefenstette, Hill, Kohli).

Example usage: train_examples, val_examples = datasets.load_dataset( 'math_dataset/arithmetic_mul', split=['train', 'test'], as_supervised=True)

Objective: This study investigates the Euclidean geometry learning opportunities presented in Further Education and Training (FET) mathematics textbooks. Specifically, it examines the alignment of textbook content with the Curriculum and Assessment Policy Statement (CAPS) curriculum, levels of geometric thinking promoted, representational forms, contextual features, and expected responses.Methodology: The research analyzed three FET mathematics textbook series to identify strengths and weaknesses in Euclidean geometry content. This study adopted the interpretivist paradigm. The study used a qualitative research approach and a case study research design. Purposive sampling techniques were used to select the textbooks currently used for teaching. This study used textbook analysis as the data collection method. Deductive content analysis was used as a data analysis strategy. In this study, interrater reliability was used to preserve the quality of data coding and reporting among coders as a percentage of agreement between three coders (Belur et al., 2021).Data collectionThis study employed various textbook analysis instruments that were specifically designed within its framework, including the content coverage instrument, mathematical activity instrument, geometric thinking levels instrument, representation forms instrument, contextual features instrument, and answer forms instrument. 1.1.1 Content coverage instrumentThe study employed a content coverage instrument as a data collection tool, with a focus on textbook topics and subtopics. The content coverage instrument, in the form of a checklist, listed all the topics and subtopics of Euclidean geometry in the grade 10–12 curriculum and assessed whether each content was covered in the respective textbooks based on their corresponding grade levels. The aim was to provide a comprehensive assessment of the extensive range of content knowledge that students are required to acquire at each school level, specifically Grades 10-12, using a rubric. The rubric for assessment was designed to gather data and emphasised the extent of Euclidean geometry content coverage. The rubric focused on content coverage and provided a space to indicate if a subtopic was covered (by ticking) or not covered (-).A checklist form was used to gather data from the textbook tasks by indicating the topics and subtopics covered in each textbook series. The checklist was developed from the CAPS guideline document for Grades 10–12. This instrument was used to examine the selected textbook content coverage to determine the extent to which the textbooks align with the CAPS Mathematics guideline document. This instrument divided the Euclidean geometry content into three categories: Grade 10, Grade 11, and Grade 12, as stipulated in the CAPS Mathematics guideline document for FET-level mathematics. To bolster results objectivity, all CAPS checklist items were quantified using dichotomous (yes/no) responses, summarised by scoring rubrics to justify different responses. A mathematical activity form tool was developed to collect data regarding the nature of mathematical activities in both worked examples and exercise tasks within each textbook. The form was designed in the format of a rubric based on Gracin’s (2018) mathematical activity framework: representation and modelling, calculation and operation, interpretation, and argumentation and reasoning. The rubric consists of five major sections, with the first section focusing on the nature of the mathematical activities required to successfully engage with geometry questions. A rubric was provided for the nature of mathematical activities for each geometry task, which was broken down into four categories to explore the nature of tasks more clearly. The categories of mathematical activities focused on representation and modelling, calculation and operation, interpretation, and argumentation and reasoning.As this study intended to investigate the students’ OTL afforded by textbooks, an evaluation form was used to gather data. A form containing the four kinds of Euclidean geometry task types was included in the evaluation form used to examine the nature of each Euclidean geometry task. This form consisted of a list of the characteristics of each mathematical activity required to carry out the geometry tasks: Representation and modelling (R), Calculation and operation (C), Interpretation (I), and Argumentation and reasoning (A).” This form serves as a classification template, categorising tasks according to the competence the tasks demand of the students. Table 4.5 presents exemplary geometric tasks, categorised by skill, alongside corresponding evaluation indicators used to assess mathematical proficiency. A representation form instrument was utilised as a data collection instrument regarding the type type of representation used in presenting of the geometry ideas in each textbook sries (see section 3.3). A rubric was utilised to capture the type of representation, with a designated space for each task. This rubric provided a space for documenting the representation format for the tasks. To make the captured data clear, we divided the rubric into four distinct sections: pure mathematics, verbal, visual, and combined forms of problem presentation.Data analysisThis study used a qualitative deductive content analysis (QDCA) approach to analyse the collected data. In a DCA, research findings are allowed to emerge from the textbooks examined (Pertiwi & Wahidin, 2020). A deductive approach was appropriate because the codes and categories were drawn from theoretical considerations, not from the text itself (Islam & Asadullah, 2018).The researcher created nine Excel files, each with a four-column table, as shown in the figure below. Every column represents the type of mathematical activity category: Representation (R), Calculation (C), Interpretation (I), and Argumentation (A). Based on the Gracin (2018) framework, the researcher and two scorers read every worked example task and exercise task in each textbook examined in this study, extracted the mathematical activity required to complete the task successfully, and recorded it in the corresponding Excel file. If the tasks required more than one activity, the researcher considered the one that was dominantly required by the task author. The figure below shows the Excel sheet used to score the mathematical tasks for this study. To examine the geometric thinking embedded in textbook tasks, a comprehensive analysis framework was employed. This involved utilising a rubric to categorise tasks according to their corresponding geometric thinking levels, spanning from Level 0 to Level 4. For instance, tasks requiring students to define properties of a geometric figure were classified as informal deduction, whereas tasks demanding formal proofs were coded as formal deduction.The analysis process commenced with a meticulous review of worked examples and exercise tasks to identify the embedded level of geometric thinking. Subsequently, Excel tables were utilised to record the geometry levels present in Euclidean geometry tasks, and their frequencies were calculated. The results, which highlighted the predominant levels in the textbook series, were then subjected to in-depth analysis. This study classified each task based on the dimensions of Zhu and Fan's (2006) answer forms and subsequently coded the problem as depicted in Figure 4.13. In this study, the researcher conducted the process of classifying the tasks based on the answers to the question forms by reading the task questions and coding them as either open-ended or closed-ended problems.The researcher examined the types of tasks within the Euclidean geometry content in terms of their representation form and contextual features. This study used Zhu and Fan's (2006) framework to classify and code Euclidean geometry tasks found in textbooks. This study analysed the following classification of tasks: "Pure mathematical (R1), verbal (R2), visual (R3), and combined form (R4), based on Zhu and Fan's (2006) theoretical framework. In particular, each task was analysed against these representation-type categories in each textbook. An Excel table, as shown in the figure above, recorded the analysis of the representation forms.To investigate the contextual features of mathematical tasks, the researcher systematically collected tasks from each textbook and created an Excel sheet to score the type of context presented in each problem. Zhu and Fan's (2006) theoretical framework provided the foundation for categorising and coding tasks, enabling a comprehensive analysis. This study classified the tasks into two distinct categories: Zhu and Fan (2006) define application problems (C1) as tasks presented in real-life situations, illustrating practical applications of mathematical concepts. Non-application problems (C2) are tasks that lack context and solely concentrate on mathematical procedures and calculations. We coded tasks presented in situations mirroring real-life scenarios as application tasks and tasks lacking context as non-application tasks. The coded data was meticulously counted, and the frequencies were recorded in tables using Microsoft Excel, as depicted in Figure 4.13. This systematic analysis facilitated a nuanced understanding of the contextual features of mathematical tasks across the examined textbooks. This study used the CAPS Mathematics guidelines as the foundation for developing an OTL analytical tool to classify the mathematical content. The CAPS Mathematics analytical tool encompasses the content areas that students should master in all grades. Next, I outlined the OTL categories, offering comprehensive details on the interpretation and analysis of the data. To analyse the data, I used a rubric for each textbook series. The researchers conducted a thorough review of each textbook task, utilising the CAPS Mathematics document as a benchmark to

The percentage of all pupils who returned valid KS2 test results who achieved level 4 or above in KS2 Maths and, separately, KS2 English. Please note that this data has also been stratified by ethnicity and eligibility for free school meals. Education plays a number of roles in influencing inequalities in health, if health is viewed in its widest sense. Firstly, it has an important role in influencing inequalities in socioeconomic position. Educational qualifications are a determinant of an individuals labour market position, which in turn influences income, housing and other material resources. These are related to health and health inequalities. As a consequence, education is a traditional route out of poverty for those living in disadvantage. The roles of education set out above imply a range of outcomes which are not readily measurable. However, inequality is observed when looking at educational achievement. Children from disadvantaged backgrounds, as measured by being in receipt of free school meals, have lower educational achievement than other children. This indicator relates to the Public Service Agreement (PSA) performance management framework 2008-2011, as follows:• PSA Delivery Agreement 10 Indicator 2 Increase the proportion achieving Level 4 in both English and Maths at KS2 to 78% by 2011 (baseline 2007 of 71%);• PSA Delivery Agreement 11 Indicator 2 Achievement gap between pupils eligible for free school meals and their peers achieving the expected level at KS2 and KS4 (national target not specified, baseline 2006 of 24 percentage points at KS2 and 28 percentage points at KS4). The National Curriculum standards have been designed so that most pupils will progress approximately one level every two years. This means that by the end of KS2, pupils are expected to achieve level 4. Previously, target levels of attainment referred to English and Maths separately, however these are now being targeted together although statistics continue to be released by each subject separately. Legacy unique identifier: P01089

17,561 Images of Primary School Mathematics Papers Collection Data. The data background is pure color. The data covers multiple question types, multiple types of test papers (math workbooks, test papers, competition test papers, etc.) and multiple grades. The data can be used for tasks such as intelligent scoring and homework guidance for primary school students.

Abstract

To facilitate secondary analyses aimed at improving mathematics and science education, the TIMSS 2003 International Database makes available to researchers, analysts, and other users the data collected and processed by IEA's TIMSS 2003 project. This database comprises student achievement data in mathematics and science as well as student, teacher, school, and curricular background data for the 48 countries that participated in TIMSS 2003 at the eighth grade and 26 countries that participated in TIMSS 2003 at the fourth grade. The database includes data from over 360,000 students, about 25,000 teachers, about 12,000 school principals, and the National Research Coordinators of each country. All participating countries gave the IEA permission to release their national data.

IEA, the International Association for the Evaluation of Educational Achievement, has been conducting international comparative studies of student achievement in school subjects for more than 40 years. When it collected data for the first time in 1994-95, TIMSS (known then as the Third International Mathematics and Science Study) was the largest and most complex international study of student achievement ever conducted, including both mathematics and science at third, fourth, seventh and eighth grades, and the final year of secondary school. In 1999, TIMSS (by now renamed the Trends in International Mathematics and Science Study) again assessed eighth-grade students in both mathematics and science to measure trends in student achievement since 1995.

TIMSS 2003, the third data collection in the TIMSS cycle of studies, was administered at the eighth and fourth grades. For countries that participated in previous assessments, TIMSS 2003 provides three-cycle trends at the eighth grade (1995, 1999, 2003) and data over two points in time at the fourth grade (1995 and 2003). In countries new to the study, the 2003 results can help policy makers and practitioners assess their comparative standing and gauge the rigor and effectiveness of their mathematics and science programs.

Geographic coverage

The survey had international coverage

Analysis unit

Units of analysis in the study include documents, schools and individuals

Universe

The study covered curricula and textbooks, teachers and pupils at selected schools in the country

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

By gathering information about students’ educational experiences together with their mathematics and science achievement on the TIMSS assessment, it is possible to identify factors or combinations of factors related to high achievement. As in previous assessments, TIMSS in 2003 administered a broad array of questionnaires to collect data on the educational context for student achievement. For TIMSS 2003, a concerted effort was made to streamline and upgrade the questionnaires. The TIMSS 2003 contextual framework (Mullis, et al., 2003) articulated the goals of the questionnaire data collection and laid the foundation for the questionnaire development work.

Across the two grades and two subjects, TIMSS 2003 involved 11 questionnaires. National Research Coordinators completed four questionnaires. With the assistance of their curriculum experts, they provided detailed information on the organization, emphasis, and content coverage of the mathematics and science curriculum at fourth and eighth grades. The fourth- and eighth-grade students who were tested answered questions pertaining to their attitudes towards mathematics and science, their academic self-concept, classroom activities, home background, and out-of-school activities. The mathematics and science teachers of sampled students responded to questions about teaching emphasis on the topics in the curriculum frameworks, instructional practices, professional training and education, and their views on mathematics and science.

Separate questionnaires for mathematics and science teachers were administered at the eighth grade, while to refl ect the fact that most younger students are taught all subjects by the same teacher, a single questionnaire was used at the fourth grade. The principals or heads of schools at the fourth and eighth grades responded to questions about school staffi ng and resources, school safety, mathematics and science course offerings, and teacher support.