The fraught issue of teacher representation

2019 – Proposal for Policy regarding Information Communications Technology in Education

A Human-centred agenda

Consistent with the ILO, we propose a human-centred agenda for the future of work (and specifically education) that strengthens the social contract by placing people and the work they do at the centre of economic and social policy and business practice (ILO, 2019). The three principles required to achieve this are:

1.       Increasing investment in people’s capabilities

In regard to investment in people’s capabilities, we propose that lifelong learning being an expectation and requirement of the future world of work means that the role of teachers and educators more broadly will continue to be important to the conditions of work. This includes funding and supporting the work of organisations involved in upskilling people and especially those providing transitioning support for people as they move between careers across their life. Actively working to address disparities that exist between people and placing those most able to assist in their development in front of them is key.

2.       Increasing investment in the institutions of work

Conditions of human’s work continue to be important and certain rights and responsibilities need to become part of a universal labour guarantee. Technologies role within this allows for the blurring between work and non-work hours, we suggest that these changes be monitored, and time sovereignty and flexibility remain those most important elements in this exchange. “Harnessing and monitoring technology for decent work” (ILO, 2019) is the role of all institutions to track the impact of technology on workers hours and ongoing wellbeing.

3.       Increasing investment in decent and sustainable work

Consistent with the UNs Sustainable development goals we propose a continued commitment to sustainable and decent work, especially that which uses technology in line with these principles. The impact on the environment and the ability to extend human experience beyond normal means are both worthy of consideration and must be carefully balanced.

The reason we promote a human-centric agenda is because we have seen throughout history that technology can and does often increase the divide between genders, social classes and so forth. We promote the role of human beings as central to education going forward for our own ability to motivate, develop and maintain relationships and adapt our demeanour and approach in line with the needs of our students. We see the role of technology as supporting humans’ abilities to greater leverage our core humanity in the educative process rather than replacing or bypassing teachers.

Teacher-mediated technology

The core message of this policy is that equity and thoughtful use are the most important elements of technology adoption. These disparities that emerge in equity most often occur as a result of one of the two following elements:

The Digital Divide

The Human Divide

1.       The digital divide is around the hardware and software that students have available as well as the provision and access to high-speed internet and quality tools both at home and at school.

2.       The human divide is centred around the provision of education mediated by a human and to what degree. We can foresee a near-future where different countries and levels of economic development produce different levels of educational outcome based upon the differing levels of involvement with fully qualified teachers, that students receive.

Both the digital and human divide occur at the class, school and global level. Classes that are deemed ‘difficult’ could be provided different forms of technology, be provided with more adults in class, but less fully qualified teachers in front of them. At the school level, those in the remote and rural areas receive inferior internet access, inferior devices and less face-to-face instruction from fully qualified and experienced teachers (via Distance Education or as a result of difficulty sourcing teachers). At the global level whole nations or geographical areas may decide that their human capacity cannot keep pace with the population boom and opt for a ‘teacherless’ form of education that is ‘scale-able’ and above all affordable. Each of these illustrations of the digital and human divide illustrate ways that equity issues can result in a broader level of inequality, something that Neil Selwyn (2019) refers to as a ‘Two-tiered system’. We seek to further complicate this two-tiered system further into 4 forms or levels of teaching and learning, whilst also noting that this list will likely become dated and redundant in the near future. It does however illustrate different forms of instruction, rather than pedagogies, that provide differing outcomes and means of engaging with the educational process.

4 levels / forms of engaging w/ Teaching and Learning

Face-to-face teaching

Video-mediated face-to-face teaching (Distance Ed. etc.)

Group or peer-to-peer online text-mediated discussion

Entirely digital forms of teaching (without qualified teacher mediation)

This very crude overview of different forms of teaching and learning available to students illustrates the differences between primarily the top two elements and the bottom two. We expect that there is a significant drop-off at the final form of ‘entirely digital forms of teaching’ which are not mediated by a qualified teacher. This is due to the centrality of teacher-student relationships and their impact on positive student outcomes.

In line with our VGSA document, the teaching and learning that students can expect to receive within schools is consistent and standardised, often to the minute. Regardless of forms of technology, we expect that students should receive the same level of face-to-face teaching in line with these core governing documents.

Andragogy versus Pedagogy: Definitions and relevance to Technology use in schools

When considering the current means by which digital forms of teaching are applied without qualified teachers. It is worth commenting on the fact that there are two broad forms of pedagogy, which though overly reductive, can give some shape to the discussion that follows (Reid, 2019). Invariably the means of teaching without qualified, human teachers, rely greatly on Instructivist approaches because these traditional forms of direct address are most easy to adapt into the online and technological spaces.

Instructivist -          Direct Instruction: -          Explicit -          Direct -          Testable

Constructivist -          Project Based Learning: -          Humanist -          Group-based -          Collaborative

We can foresee a future where technology-mediated instruction begins to more genuinely replicate those ‘constructivist’ ideas more convincingly, which is where it will be worth considering which of the four forms of instruction are being used to deliver these forms, which may have differing outcomes for quality and engagement.

Before moving on from the topic of means of engagement with learning environments it is prudent to discuss the difference between andragogy and pedagogy. Where Andragogy literally means "leading man", whereas "pedagogy" means "leading children". This dialectic difference is not mere semantics. The rise of MOOCS and similar online learning pathways are made available to adults, who often sacrifice some of the quality of education for the benefit of convenience, less social aspects, fewer relationships at the expense of an ‘available-any-time’ approach. It is crucial that these two approaches to learning are not conflated and that students are taught in ways consistent with pedagogical approaches and that adults do not make decisions that they themselves would accept, but which will negatively impact on students who are most in need of the core social, participatory and democratic elements of their schooling.  

‘Datafication’ and ‘learnification’ of education systems

Biesta (2009, 2015) describes the ‘learnification’ of education as an element of a broader Neoliberal ad performativity agenda (Ball, 2003, 2016). In short, it means that the ultimate goal of learning is success on tests, largely standardised tests and as such schools are becoming ‘learnified’ around these concepts. This is a dominant ideology, despite running directly counter to other narratives calling for broader ranges of skills such as ’21^st century skills, ‘general capabilities’ and so forth. The ‘datafication’ of schools runs along similar lines (Lycett, 2013; Stevenson, 2017)

A brief summary of three major movements within technology in Education

This diagram indicates three major considerations for the future of education mediated by technology. This is not an exhaustive list of elements but provides key talking points for impact upon students and teachers as well as the educational systems as a whole.

 

 

These three elements rely upon one another and separating them is illustrative rather than actual. As algorithmic thinking, personalisation, artificial intelligence all rest on the elements of big data and learning analytics. However, the key noticeable and most problematic element is that there is no human, teacher element within this triptych this conflict will be explored below.

For each of these sections the focus is one of ethical considerations and framing with monitoring to ensure these principles are being met.

Algorithmic thinking and personalisation – It is worth noting that ‘Artificial Intelligence’ (AI) is just an advanced form of an algorithm that learns as it operates. But nonetheless, algorithmic thinking is by itself a threat to the human-mediated curriculum that we deliver, due to concepts such as ‘management by algorithm’, ‘incomprehensible assessment by algorithm’ and ‘personalisation without person’. To explore each concept in detail, management by algorithm is a means by which human behaviour is managed without direct interaction with a human being and is something that should be challenged and repelled from within educational spaces. Assessment practices in schools that focus on rubrics and move away from student immediate comprehensibility can make the jump to algorithmic thinking whereby the result gained cannot be ‘reverse-engineered’ by the students and are therefore less clear and increase the divide between students who are more literate than their peers. Personalisation as an agenda emerged from the colloquially known ‘Gonski 2.0’ report (DET, 2018) and is at the ideation phase for appearing as a tool, aimed as a tool for teachers rather than a replacement (Donovan, 2019). The personalisation agenda is something being actively push by many ‘Big Tech’ agents and in most situations minimises the teacher’s role, or dramatically changes it from the traditional ‘sage-on-the-sage’ to the ‘guide-on-the-side’. This shift is not inherently bad and in many reckonings is a positive, but it remains a shift that needs closer consideration and awareness paid to it by educators, unions and international bodies.

Big Data and learning analytics

Schools are awash with data (Balacco, 2010; Hattie, 2005) and big technology companies and a whole suite of unregulated and uncontrolled apps routinely collect copious amounts of often sensitive data from our students. Data is widely considered ‘the new gold’ of modern commerce, being traded, scraped over and analysed to improve products and profit margins. The groups and people who control and command the largest quantity and quality of data are and will continue to be the most powerful agents in the global space. The ideas that teachers and Students are being used as guinea pigs and viewed as mere data-points for collecting information in regards to informing improving products and educational offerings is problematic. This idea runs directly counter to inalienable human rights and undermines the humanity and democratic nature of our societies. It is generally understood that data is beyond the pail of comprehensibility in that it is no longer possible to realistically unravel the ways that data has been collected and used to deliver customised offerings to you. This has ramifications for the ways that this concept is challenged, in that it is too late to put the genie back into the bottle. What must instead occur is a re-emphasis on the ethical use of this data and the rights of individuals in the face of these complex algorithms that re-constitute data in ways that are incomprehensible to the lay-person. Implicit within this discussion is the exceptional value of student’s data and the patterning of students as committed consumers to various software and hardware in their most formative developmental years. As such, this space is an area for ‘white-hat’ experts to be trained and supported by Governments or NGOs to monitor and protect their citizens from these complex and powerful forces. Grand standardised and samples-based tests fit within this umbrella, with tests like NAPLAN, PISA, TIMSS and PIRLs all being a more global manifestation of these ‘Learnification’ and ‘Datafication’ of schools.

Artificial Intelligence

“Australia is in a unique situation as the only Western democracy without comprehensive enforceable protection of human rights (that is, no bill of rights, no comprehensive constitutional protection of rights).” (Daly et al., 2019)

The increasing importance and emergence of AI seems unlikely to ease and “Blocking all of these technologies is not an option, any more than cutting off access to the internet would be, but there may be scope to ban particularly harmful technologies if they emerge.” (Emphasis added, Page 15, Dawson, et. al., 2019). The identification obligation is key and “People should always be aware when a decision that affects them has been made by an AI, as difficulties with automated decisions by government departments have already been before Australian courts” (Page 7, Dawson, et. al, 2019). For monitoring purposes, these types of issues should be made visible and transparent long before litigation is required.

The question of expertise, knowledge and transparency are key as “Full transparency is sometimes impossible, or undesirable (consider privacy breaches). But there are always ways to achieve a degree of transparency. Take neural nets, for example: they are too complex to explain, and very few people would have the expertise to understand anyway.” (Page 11, Dawson, et. al, 2019). As with the above examples, a ‘Humans-in-the-loop’ (Page 33, Dawson, et. al, 2019), or, in this case, a qualified expert in the loop doing the monitoring of these technologies.

A more targeted set of AI responsive goals will be expanded through the ‘rights-based, human approach’ section.

Concluding the three major movements within technology in Education

The low status of the teaching profession in nations around the world hold significant threats in an era where teacher replacement is technologically possible and economically a great deal more cost-effective. In this respect the two issues of low teacher status and technological advancements in AI, Data and Algorithmic thinking need to always be considered alongside one another.  Greater need for quality teachers as the move towards lifelong learning and career flexibility generally suggested by future prognostication.

 

 

3 proposed solutions

As Reid (2018) posits, more time needs to be spent focusing on the goals of certain interventions, beyond the earlier Gillard goal of improving PISA results and brining in 1-to-1 devices. Too often in the technology space little thought is committed to focussing on the ‘why’ of technological developments. In this space, many elements of technology adoption in schools have been assumed, the introduction and continued rollout of AI and Machine-learning into schools allows us a unique opportunity to set up ethical frameworks and problematise this technology within schooling. Here are three proposed solutions that allow for monitoring and pause for consideration at this timely moment.

Firstly, Dawson, Schleiger, Horton, et. al (2019) suggest that all AI systems should and must have a ‘human-in-the-loop’ (HITL), but in educational settings this is not sufficient, what is required is a ‘Qualified-Teacher-In-the-loop’ (QTIL). Second, there is also space and room for a new role within schools, an empathetic and human data manager who is responsible not only for upskilling teachers around data, its humane collection and use, monitoring, security and stewardship. Lastly, there is space for a monitoring group who are responsible for monitoring these three forces, and others as relevant.

A rights-based, human-focused agenda

Generic human rights

Rights to:

-          Disappear

-          Disconnect

-          Manage own data

Teacher rights

Rights to:

-          Be unavailable / off-the-clock from work requirements

-          Choose appropriate tools without bias

-          Refuse to use dictated tools

-          Control own resources and materials

-          Avoid double-handling

-          To produce content and share willingly via creative commons

-          Share all intellectual property as freely as possible

-          Be tech company blind and agile around tool use

-          Recognition for resources created and work completed

-          ‘Whistle-blow’ around poor practices in the tech space

-          Avoid commercial incursion into professional identity

-          Avoid conflicts of interest

Student rights

Rights to:

-          Not appear on social media without express permission

-          Own classwork and have agency and influence over such works wider use in regards to promotion

-          Agency around key educational decisions within reason

-          Trust in the safety and protection and safe stewardship of their data

A rights-based, human-focused agenda on AI

The following ethical guidelines are modified from (Dawson, Schleiger, Horton, et. al, 2019) and have been shaped and edited to more closely suit the focus of educational AI and the problems that it poses.

1) Right to Transparency. All individuals have the right to know the basis of an AI decision that concerns them. This includes access to the factors, the logic, and techniques that produced the outcome.

2) Right to Human Determination. All individuals have the right to a final determination made by a person.

3) Human-in-the-loop. All AI systems must have a human person within the system. (Dawson, Schleiger, Horton, et. al, 2019)

4) Identification Obligation. The institution responsible for an AI system must be made known to the public.

 5) Fairness Obligation. Institutions must ensure that AI systems do not reflect unfair bias or make impermissible discriminatory decisions

6) Assessment and Accountability Obligation. An AI system should be deployed only after an adequate evaluation of its purpose and objectives, its benefits, as well as its risks. Institutions must be responsible for decisions made by an AI system.

7) Accuracy, Reliability, and Validity Obligations. Institutions must ensure the accuracy, reliability, and validity of decisions.

8) Data Quality Obligation. Institutions must establish data provenance and assure quality and relevance for the data input into algorithms.

9) Termination Obligation. An institution that has established an AI system has an affirmative obligation to terminate the system if human control of the system is no longer possible.

10) Comprehensible origins. As all AI systems replicate and magnify human biases and subjective decisions, each AI system needs to provide a logical thought piece, or literature review explaining the thinking and ideas that underpin its processes.

Students

Students need to be offered agency and co-agency over their learning and their environment. In addition to this the OECD dictates three further forms of Key Competencies: ‘Creating new value’; ‘reconciling tensions and dilemmas’; and ‘taking responsibility’ (Howells, Page 5, 2018). In particular Howells stresses that students when “dealing with novelty, change diversity and ambiguity assumes that individuals can think for themselves and work with others” (p6). This agency and co-agency exist alongside teacher agency, whereby teachers are empowered to use their professional judgement, skills expertise and pedagogical freedom to choose what pedagogical and classroom approaches are most suitable in their context. Whilst agency exists on both sides, the core element that ties teachers and students together is a shared professional relationship with which the two parties mediate a mutually beneficial environment to meet needs consistent with the teacher’s worldview and that of broader society. Notably, we as a union and as a society disagree with any pedagogical approaches that do not hold this as central to its beliefs. Indeed, “To help enable agency, educators must not only recognise leaders’ individuality, but also acknowledge the wider set of relationships – with teachers, peers, families and communities – that influence their learning. A concept underlying the learning framework is “co-agency” – the interactive, mutually supportive relationship that help learners to progress towards their valued goals. In this context, everyone should be considered a learner, not only the students but also teachers, school managers, parents and communities.” (Howells, Page 4, 2018). This broadening of everyone as learners is important but considering the climate of the role of teachers and educators, we also strongly emphasise teachers expertise and agency as professionals. In short, “It is time to shift the focus of our students from “more hours of learning” to “quality learning time”. (p6)

Teachers

Teachers never were consulted or worked with alongside in regard to hardware or software beyond the most tokenistic fashion. This is not a goal that we should expect or promote. Rather there is a space for regulation and monitoring of the global Edu-business and ‘Big Tech’ or ‘Big data’, in both the positive (UNESCO Institute, 2019), but also crucially, the negatives. What is required and asked for from teachers may be untenable or unrealistic. In an ideal world, without restrictions of time, teachers would be immune to ‘platform capitalism’ and able to pick up and drop different apps, software and tools with equal skill and ability. They would find no need for accreditations, unpaid or paid endorsement deals or feel the need to supplement their income or profile using the above means. As these things may be too difficult to achieve, instead we propose the ethical approach and rights-based ideas outlined above. One key innovation in this space will be the generation and extension of the above rights-based approach towards an agreed upon ethical model that teachers can opt-in for, as a ‘Big Tech’ neutral type of accreditation. Such an accreditation would allow teachers to show their commitment to SDGs, active monitoring and challenging of ‘Big Tech’ and ‘Big Data’ into their schools. These types of accredited teachers would assist and tap into the activities of the broader regulatory bodies and serve as ‘on-the-ground’ experts who can communicate with and exchange ideas with such crucial regulatory bodies.

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