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Data analysis

Why data? 

Data has become a critical driver of decision-making.

"Without data, you're just another person with an opinion"

W. Edwards Deming

Analytics for teachers

Teachers are often expected to analyse data in order to improve their performance. Collecting and analysing data supports teacher inquiry. You can gather and analyse data to identify at-risk students, inform your inquiry, and quantify the impact your interventions are having on student learning.

Image of Analytics

Image by Tumisu

Data provides you with:

  • a broader overview of students' needs
  • the ability to make well-informed decisions
  • the ability to measure the effectiveness of strategies and interventions
  • a greater awareness of where to spend time, effort, and resources in order to maximise impact
  • the ability to provide accountability through evidence-based approaches. 

Student data is more than just test scores. It comes from a variety of sources to give a fuller picture of student learning. Being able to access shared data via a student management system (SMS) enables teachers to identify areas of need, organise student groupings, and work collaboratively with students and whānau to set learning goals.

Student inquiry

Schools gather a range of student data, including aspects of a student's identity such as:

  • demographics – race, age, gender, economic status, special education needs
  • academic history – past results, subjects taken.

Although this data may be useful to support a teacher inquiry, it is usually the student data that can change that will be most relevant to teacher inquiry. This includes:

  • performance in assessment and achievement data  test results, completion rates
  • behavioural data – rewards for positive behaviour, number of incidents, disciplinary actions taken, attendance
  • engagement with resources, materials, and tools – how much a particular resource is being used by students, frequency of accessing digital tools and devices for learning and collaboration
  • engagement in learning – how many times a student asks a question, answers a question, contributes to discussion, uses a digital device during the activity/lesson 
  • student voice – to what extent are students able to articulate their learning goals and express how they are achieving these?

The right data in the right format can inform teacher practice and student learning goals. Privacy and data security must be considered when sharing data with whānau and other schools.

Source: Data Quality Campaign  

Key resource

What is student data?

A visual overview of different types of student data presented as in infographic by Data Quality Campaign.

Quantitative data

Quantitive data is numerical information, such as: 

  • days absent from class
  • test results
  • number of behavioural incidents
  • number of times a resource was accessed.

Quantitative data can be easily entered into a spreadsheet and visualised in tables and graphs. 

Qualitative data

Qualitative data is descriptive, rather than numerical. It can provide insights into what your students are thinking and feeling about their learning. 

Methods for gathering qualitative data include: 

"Qualitative data can put flesh on the bones of quantitative data"1

  • interviews
  • photographs
  • video recordings
  • reflections
  • observations
  • open-ended questions in a survey

Qualitative data should be used in conjunction with quantitative data in order to triangulate (validate) your results. 

Categorising qualitative data

Coding and categorising qualitative data into numerical data sets makes it easier to visualise and analyse. 

Use the insights, revealed by collecting and analysing student feedback data to inform your actions.

Example of quantifying qualitative data to inform an inquiry on flipped learning
Student Response Category
1 I liked the video Positive
2 Boring Negative
3 I can focus more at home so it was good for me. I liked how we just spent class time doing group work Positive
4 The forums were a good way to talk about the readings and the video at home Positive
5 My chromebook couldn't access the work Negative
6 I like chatting on the forums Positive
7 The video was good Positive
8 No Negative
9 No response
10 I couldn't log on to access the work. It was frustrating Negative
11 The forums were cool Positive
12 I liked having more time to do group work in class Positive
13 I liked the forums Positive
14 Chatting with classmates on the forums Positive
15 The video was interesting Positive
Thematically categorising qualitative data

Organising or coding your qualitative data into thematic categories can give a more accurate picture about your teaching and learning context.

Themes Respondents
Liked more time for group work 2
Liked the forums 5
Liked the video 3
Technical problems 2
Didn't enjoy it  2
No response 1

1. Shaddock, A., & Hattie, J. (2014). Using data to improve learning: a practical guide for busy teachers. Camberwell: ACER Press.

There are several tools available for helping you collect, analyse, and visualise data, ranging from professional software to tools designed for casual users.

  • Piktochart  – A design app for making infographics. It contains tools for visualising data in charts and graphs. It has both free and premium accounts.
  • Canva  – A graphic design app with an easy to use infographic maker. 
  • Tableau  – Software for analysing and visualising data. Free to download and use, and has a premium account for added features. 
  • NVivo  – Software for analysing qualitative data. 

More information »

Using data to improve learning  – book by Anthony Shaddock
Key resource

Using data to improve learning: A practical guide for busy teachers

A step-by-step approach to using data for improving learning by Anthony Shaddock.  

The technology-rich environment of schools provides easy access to student data. Collecting data to inform teacher inquiry, and classroom teaching and learning is common practice in all schools.

Collecting data can be construed as an invasion of privacy. When collecting, sharing, and reporting on data, ensure you:

  • comply with your school's code of practice
  • seek approval for your inquiry and methods of data gathering from your team leader  
  • stay mindful of ethical issues that could arise out of collection of data, especially if that data is sensitive or likely to provoke strong feelings
  • do not share the results with anyone other than the intended group.
Assessment Online logo

In New Zealand schools, it is important for leaders and teachers to analyse assessment and attendance data in order to gauge student progress in relation to national standards, as well as provide accountability to Board of Trustees, and Ministry of Education. While you may use this data to inform an area for inquiry, this is a different purpose for data gathering and analysis. Assessment Online  provides information about gathering, analysing, interpreting, and using information about students' progress and achievement in relation to The New Zealand Curriculum.

Key resource

Ten trends 2016: Data-driven organisations  

An explanation of why using data is an important focus for education going into the future from CORE Education. 

Using data to support teacher inquiry

Technology has expanded our horizons and there is an obvious role for teachers to engage with data to assess what works for them, in their school, with their students.

Using data to improve learning: A practical guide for busy teachers

Using data can turn hunches into credible, evidence-based springboards for taking action

Teachers and social media

As teachers, we all have hunches about what isn't working and what could work better for our students. Timperley, Kaser, and Halbert (2014), define developing a hunch as a vital phase in their Spiral of Inquiry framework

Without evidence to back them up, hunches remain subjective. The well-planned use of data in an inquiry can turn a hunch into a credible, evidence-based hypothesis and call-to-action.

Use data to:

  • confirm hunches
  • reveal new patterns of insight
  • justify change 
  • motivate colleagues, stakeholders, and school leaders into taking action.
Key resource
Centre for strategic education image

A framework for transforming learning in schools: Innovation and the spiral of inquiry

Helen Timperley, Linda Kaser, and Judy Halbert outline their model for teacher inquiry.

More information »

Inquiry scenario 

High School science teachers inquire into improving outcomes for at-risk students by introducing flipped learning.

Teacher inquiry cycle steps Actions taken
1. Scanning – What's going on for learners?

Achievement data reveals that at-risk students are unable to demonstrate progress in understanding of key indicators.

Submission rates of assignments and homework tasks are low.

Teacher observations describe some students as presenting signs of disengagement from classroom learning.

2. Focusing – Where will concentrating our energies make the most difference?

Teachers collect student feedback through a questionnaire. They gather qualitative feedback from students regarding likes and dislikes of classroom and assignment/homework tasks.

Teachers analyse the data. Qualitative feedback shows that students are:

  • having trouble concentrating in class
  • getting bored in class
  • prefering collaborative tasks to individual assignments.  
3. Developing a hunch – How are we contributing
to the situation?

Teachers reflect on how they might be contributing to the situation.

They wonder whether there might be too much teacher-talk or lecture-style presentation of key concepts in the classroom.

4. New learning – How and where will we learn more about what we do?

Teachers collaborate and consult experts within the school or Community of Learning | Kāhui Ako.

They consider best practice pedagogy and research on student engagement and motivation. They find flipped learning:

  • allows students anytime/anywhere access material covering key concepts
  • frees up classroom time for more student-led collaborative work.
5. Taking action – What can we do differently to make enough of a difference?

Teachers introduce a flipped learning component to the next assignment.

The component contains:

  • intended learning outcomes and NCEA achievement standards
  • engaging multimedia to present key concepts
  • quizzes to practice key concepts
  • discussion questions to be conducted in a Moodle chat forum that has been set up especially for the class. 

Teachers use analytics in Moodle to monitor how many times students have accessed the flipped learning material. 

Teachers use the analytics embedded in the forum tool to monitor how many times a student has posted or replied to a classmate's post. 

6. Checking – Have we made enough of a difference?

Teachers collect and represent all data using appropriate visualisation tools. They consult data to analyse whether their flipped learning intervention has improved outcomes.

They:

  • use the Moodle analytics to get a profile of student engagement in the flipped learning task 
  • collect and analyse new achievement data
  • conduct final feedback questionnaire with students
  • triangulate all of the above data sets to get a fuller picture of the impact of the flipped learning assignment.   

Teachers then share and report on data with colleagues, and students where appropriate.

Teachers use data to justify changes going forward.

More examples »

Learning analytics are defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.”

Learning analytics: drivers, developments, and challenges

Mark Osborne discusses the implications of learning analytics for education in this EDtalk.

Learning analytics captures data on specific, observable behaviour in real time.

Informing pedagogical action: learning analytics with learning design

Many virtual platforms such as Moodle and Google classroom have built-in analytics which provide teachers with information about the digital learning activities of their students. By following students' digital actions in online learning spaces, teachers can access data to inform next steps.

Learning analytics harnesses the potential of digital learning, enabling teachers to:

  • provide increasingly personalised, meaningful, engaging learning experiences for students
  • track student progress
  • get early intervention information as soon as possible
  • make informed decisions about strategies that are most likely to make a difference for students.

More information » 

Teacher inquiry is an effective method to help teachers gain a better understanding of their classroom practice. By developing reflective practitioner competencies, it contributes to teachers' self-evaluation and improvement. Emerging teaching and learning analytics technologies are at the spotlight of the research and practice communities, globally to facilitate this process.

Demetrios G. Sampson

Learning analytics and the collection of student data can play a critical role in supporting the various steps of the teacher inquiry cycle

Learning analytics

Outcomes of learning analytics

Learning analytics outcome Description
Discover patterns within student data                                                                            Helps teachers provide more informed support and scaffolding to individual students and conduct more holistic assessments.
Predict future trends in students’ progress Elicits how the students might perform in the future, for example in terms of performance or engagement, and plan appropriate support.
Recommend teaching and learning actions Recommendations may refer to educational resources/tools or learning/assessment activities that are appropriate to meet the individual needs of students. The recommendations may be addressed to either teachers or students.

Table source: Demetrios G. Sampson

 Tools to identify patterns within student data

Moodle

 

  • Learning analytics enriched rubric   – An enriched rubric containing some criteria and related grading levels that are associated to data from the analysis of learners’ interaction and learning behaviour in a Moodle course, such as number of post messages, times of accessing learning material, and assignment marks.
  • Level up!  – A Moodle plugin that gamifies learning by converting student progress data into experience points.
  • Forum graph  – Moodle plugin that visualises interactions between students on chat forums. Reveals information such as which students are leading discussions and which students in particular are in dialogue with each other.
  • Moodle learning analytics  – A video explaining the learning analytics capabilities embedded in Moodle.

SNAPP

A chat forum analytics tool for Sakai. Provides heat maps describing the interactivity of students on chat forums and social networks. 

Tools to predict future trends in students’ progress

Student success system

Analytics and visualisations provide diagnostics that help teachers spot at-risk students. Part of the Brightspace LMS.

X-Ray learning analytics

This plugin developed by Moodlerooms is a comprehensive learning analytics that allows teachers to discover patterns in student data, such as engagement and performance in learning, as well as predict future trends. 

Engagement analytics

This plugin for Moodle measures student engagement and identifies at–risk students. 

Tools that inform and recommend teaching and learning actions

SmartKlass

A plugin for LMSs (including Moodle) that analyses student performance and tracks their engagement with resources and materials.

Knewton

Knewton

An adaptive learning platform that gauges what students already know and recommends pathways for learning. Teachers can draw on Knewton's open-source course content to set assignments and track student progress. Use inbuilt analytics to get a fuller picture of students' learning contexts.

Key resource
Khan Academy

Khan Academy Coach

Analytics help parents and teachers monitor student progress at a glance. It also provides recommendations for future actions.

Using data to improve learning  – book by Anthony Shaddock
Key resources

Using data to improve learning: A practical guide for busy teachers

A clear, step-by-step approach to using data for improving learning by Anthony Shadrack.   

Centre for strategic education image

A framework for transforming learning in schools: Innovation and the spiral of inquiry

Helen Timperley, Linda Kaser, and Judy Halbert outline their model for teacher inquiry.

Learning analytics community exchange (LACE)

An EU funded project that coordinates communities working in learning analytics.

Data quality campaign

Data quality campaign

A US resource on data policy and use in education.

Assessment Online logo

Assessment Online  

Information for NZ school leaders and teachers about gathering, analysing, interpreting, and using information about students' progress and achievement.

Readings 

Learning analytics: drivers, developments and challenges

A review of learning analytics, beginning with an examination of the technological, educational, and political factors that have driven the development of analytics in educational settings.

Informing pedagogical action: Aligning learning analytics with learning design

How learning analytics can inform pedagogical action.

Informing learning design to improve teacher inquiry

An overview of current research into the connections between learning design and learning analytics.

Discovering patterns within student data

Learning analytics enriched rubric

An enriched rubric containing some criteria and related assessment levels that are associated with data from the analysis of learners’ interaction and learning behaviour in a Moodle course, such as number of post messages, times of accessing learning material, and assignments.

Level up!

A plugin that gamifies learning by converting student progress data into experience points.

Forum graph

A plugin that visualises interactions between students on chat forums. It reveals information such as which students are leading discussions and which students are in dialogue with each other.

SNAPP

A chat forum analytics tool. Provides heat maps describing the interactivity of students on chat forums and social networks. 

Predicting future trends in students’ progress

Student success system

Analytics and visualisations provide diagnostics that help teachers spot at-risk students. Part of the Brightspace LMS.

X-Ray learning analytics

This plugin is a comprehensive learning analytics tool that allows teachers to discover patterns in student data, such as engagement and performance in learning, as well as predict future trends. 

Engagement analytics

This plugin measures student engagement and identifies at-risk students. 

Recommending teaching and learning actions

SmartKlass

A plugin for LMSs (including Moodle) that analyses student performance and tracks their engagement with resources and materials.

Knewton

Knewton

An adaptive learning platform that gauges what students already know and recommends pathways for learning. Teachers can draw on the open source course content to set assignments and track student progress. The in-built analytics give a fuller picture of students' learning contexts.

Khan Academy

Khan Academy Coach

Helps parents and teachers monitor student progress. It also provides recommendations for future actions.

Piktochart

An design app for making infographics. It has tools for visualising data in stylish charts and graphs. It has both free and premium accounts.

Canva

A graphic design app with an infographic maker. 

Tableau

Software for analysing and visualising data. Free to download and use, with a premium account for added features. 

NVivo

Software for analysing qualitative data. 

Commonly used graphs

This section on Assessment Online explains different types of graphs and their uses.

The 38 best data visualisation tools

Explanations of and, links to, data visualisation tools on Creative Bloq's blog.

e-Learning community discussions

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