Achievement refers to the totality of skills and attributes embedded within the four capacities of Curriculum for Excellence and developed across the curriculum in school and through learning in other contexts.

Attainment refers to the measurable progress which children and young people make as they progress through and beyond school.

This progress is measured in relation to curriculum areas and in the development of skills for learning, life and work.

Big data can refer to large scale assessment and achievement data that are collected and collated, often at a national or international level.

Examples include: international student assessments, national education statistics, various large scale surveys etc.

Large data sets can be analysed to reveal patterns, trends and associations or correlations.

Data analysis involves examining information and breaking it down for understanding and meaning.

Data collection refers to the gathering of information from a variety of sources.

Data culture is the collective behaviours and beliefs of people who value, practice and encourage the use of data to improve decision-making.

Data driven dialogue is a structured process for reviewing data and managing data discussions. It consists of 5 phases: Prediction, viewing the data, observation, inferences and next steps.

The data driven dialogue process builds awareness and understanding of viewpoints, beliefs, and assumptions about data while suspending judgments.

The phases of data-driven dialogue assist individuals and groups in developing a shared understanding of data through achieving a clarity of focus.

Data literacy refers to the ability to explore, understand and communicate with data in a meaningful way.

Data science refers to visualising data, summarising it in various ways or using it to predict events or outcomes.

It usually consists of gathering large sets of data, using computer algorithms to find patterns in the data. Those patterns are then used to understand processes or make predictions in order to plan for and respond to events. 

Data synthesis involves examining multiple sources of information in order to create a coherent picture through the identification of patterns, connections and relationships.

Demographic data is descriptive information about the school community, such as SIMD, FME, attendance, exclusions, EAL, ASN etc.

Evidence can be considered to be information used to demonstrate impact and to support decision making. Evidence can be data, in many forms, and also the knowledge base about what works. 

Formative refers to processes by which teachers and learners use information about student achievement to make adjustments to the students learning to improve their achievement.

Input data defines what the school are doing to get the results they are getting, such as programmes, pedagogy, interventions. 

Intersecting data based on the work on Victoria Bernhardt, considers four categories of data.

1. demographics
2. school processes
3. pupil learning
4. perceptions

These provide a wider picture of learner achievement.

Each measure in itself can provide useful information but by intersecting data categories it can enhance the level of analyses and allows for different questions to be explored.

Output data is data related to pupil attainment and achievement, such as CfE, SNSA, SCQF etc.

Perception data helps us to understand what stakeholders think and can be gathered through surveys, questionnaires, feedback etc.

Qualitative data is non-numerical data which deals with descriptions of what can be observed and recorded such as questionnaires, feedback etc.

Quantitative data is data in the form of numbers which can measured, such as attendance levels, attainment, leaver destinations etc.

Small data is described by Sahlberg as tiny clues found in school that can uncover important relationships about teaching and learning. Big data can reveal correlations but small data can reveal causation.

Small data may come from teachers’ and learners’ purposeful observations, formative assessments, and reflections of what is happening during teaching and learning processes in schools to reach collective human judgement to understand what is happening.

Statistical significance refers to the probability that a particular result of a statistical test could be due to chance factors alone and can describe the statistical uncertainty of a result.

Summative refers to periodic summaries of progress and achievement for reporting and monitoring.

Triangulation is the process used to ensure evaluative statements about strengths and aspects for development are grounded in a robust evidence base.

Triangulation involves bringing together evidence-based information from quantitative data, people’s views and direct observations of practice.

Triangulation should involve all school staff, learners, partners and other stakeholders.