Coined in 1964 and popularised in the 1970s via Alvin Toffler’s Future Shock, information overload amounted to mental paralysis: presented with too much data, you were left confused and/or unable to make a decision.
The human brain has not developed as much as technology since 1970. However, expectations of workers, including teachers, to deal with and produce more information have risen dramatically. Technological advances have led to more, not less work and management and administrative tasks that didn’t exist a generation ago.
In 2019, The Educator reported that Australian lower secondary teachers spent 24.9 hours per week on non-teaching tasks compared to the OECD average of 18.2 hours.
Valuing the Teaching Profession: An Independent Report, (known as the Gallop Report), commissioned by the NSW Teachers Federation, found the “intensity and complexity of teachers’ workloads” had ramped up “enormously”.
Report co-author Patrick Lee, former IEUA NSW/ACT Branch Deputy Secretary, said most submissions from teachers to the review went to “the clash between the time available to do the work teachers do and the other stuff you’ve been asked to do”.
These time-consuming duties include: excessive documenting of programs; data collection prioritised over teaching time; system initiatives such as data walls; and the datafication of learning, with teachers required to upload data at every turn.
Impact on workload
In early 2021, the IEUA NSW/ACT Branch surveyed members on the impact of requirements in the Nationally Consistent Collection of Data on School Students with Disability (NCCD), finding most respondents had to submit the same or similar data on multiple platforms and had issues with uploading data during peak times.
“The NCCD process has a significant impact on workloads for learning support teachers, with nearly half of all respondents reporting they receive no additional release time and are undertaking five or more after-school hours per week to complete the process,” the survey found.
In 2018, the review of Victorian teacher workloads conducted by consultancy Nous Group found 88 percent of teachers reported assessment, feedback and administrative tasks had increased their workload in the last three years, and 72 percent of them believed technology had increased their workload in the last three years.
What is to be done?
Advocates of data-informed practice say the problem isn’t how much data we encounter, but how we engage with it.
Data-informed practice is “the simple act of using tangible evidence and information to make decisions and draw conclusions”.
A report from the Association of Independent Schools of New South Wales (AISNSW) says, “Without focused questions, the collection, analysis and use of data may be scattered, unclear and pointless.”
It says data-informed practice in schools should be “the systematic use of data by schools and educators to improve student learning, specific instruction, classroom practices and overall wellbeing”.
“For data to be most useful, it should be collected systematically and for a clearly identified purpose.”
The Grattan Institute’s report Targeted teaching: How better use of data can improve student learning states: “Like a doctor trying to identify what treatment patients need to improve their health, teachers need to identify what teaching their students need now to improve their learning.”
Schools need “systematic collection of high-quality evidence of student learning, to analyse this evidence to identify learning gaps and to monitor progress over time, and to use this evidence to identify successful teaching”.
Targeted teaching positive feedback loop
- Assess what each of our students knows already
Identify a baseline for every student on an agreed learning progression to:
- assess current understanding
- agree appropriate learning goals
2. Target teaching to meet each student’s learning needs
Use current achievement data to:
- plan how to cover the next topic
- target teaching to address what each student is ready to learn next
- refine teaching using frequent formative assessment.
3. Rigorously track the progress of all our students
Monitor progress of every student to:
- reassess their understanding
- analyse progress vs learning goals
- support any student who is stalled
- provide individualised feedback
4. Adapt our teaching practices to improve next time round
Analyse progress and outcome data to select and refine teaching practice:
- keep doing what works best
- improve or stop what doesn’t.
Data applied: A case study
A prime example of the benefits of data-informed practice comes from Duval County Public Schools in Florida, in the United States.
Duval, engaging with a data analytics company, created a customised system to track metrics like attendance, discipline reports and test scores to flag at-risk students, increasing graduation rates by over 25 percent in 10 years. Duval’s successes came despite more than half of its 130,000 students being “economically disadvantaged”.
The system provided administrators and teachers access to a “visual dashboard” which helped them identify students in need of “academic guidance, tutoring, dropout prevention support services or accelerated credit programs”, reports govtech.com.
Duval’s Graduation Rate Initiatives Team (GRIT) Research Director Saul Bloom said his staff also uses the data to “examine instructional policies and practices to improve performance outcomes”.
The digital data system uses a high school graduation tracker, at-risk graduation tracker and early warning systems to provide current student data and “predictive trend-based analytics” that address achievement gaps.
Will technology supersede data?
If the vision of educational consultant Aliki Constantinou is realised, more (advanced) technology will reduce the reliance on data analysis.
He believes a “hybrid model of learning” is approaching, in which students interact with artificial intelligence (AI) software as well as a teacher.
He says these platforms will have the “inbuilt capacity to conduct thorough knowledge audits to gauge an individual’s current level of understanding”.
Constantinou believes such AI can lead to “tech-assisted Socratic learning” where “the role of the teacher is to guide their students to understanding, rather than to simply relay knowledge”.
These next generation solutions rely on advances in natural language processing (NLP), to empower students to “take ownership of their own learning journey”.
This allows them to “pose individual questions – either verbal or typed – and receive succinct and accurate answers from their own digital teaching assistant”. NLP platforms instantly target the right data, to ensure that individuals learn effectively and only get relevant facts.
This level of AI doesn’t just react to user questions, it can “proactively initiate conversations with users”. Constantinou believes this will improve critical thinking skills.