The Technological-Industrial Complex and Education - book overview

 This book, published in 2024 by Springer and written by S.M.S. Curtis, V. Desimoni, M. Crumley-Effinger, F.D. Salahan and t.d. Jules is titled 'The technological-Industrial complex and education: Navigating algorithms, datafication, and artificial intelligence in comparative and international education. 

The authors are academics from Universities in the US of A. 

There are seven chapters.

The first chapter - AI in comparative and international education (CIE) in the age of the anthropocene, sets out the rationale for the book and lays out the argument for a human-centred approach to AI. 

Chapter 2 'the rise of the technological-industrial complex and education 4.0' summarises the connections between Education 1.0 to 4.0 and Web 1.0 to 4.0. The chapter argues that education is connected to the developments and expansion of the Web. Education tends to lag behind the Web developments. In the context of CIE which began as colonisation projects, the process of decolonisation is important to help assure justice and equity for the benefit of all.

The third chapter 'the emergence and progression of AI in CIE) summarises the evolution of AI and cautions the utilisation of AI with the need for ethics.

Chapter 4 then continues on with 'beyond the anthropocene: ethics, equity and the responsible use of AI in CIE.

Following, 'using AI for educational research: methodological implications. Various ways AI may be useful including AI-powered conversational robots, machine learning, natural language processing and predictive analytics tools, provide opportunities for education research. Ethics is a key to how these are deployed.

Chapter 6 'regulatory responses and emerging global scripts in the governance of AI in education (GAIE). Various efforts at regulation are presented and discussed. Countries include the EU, Turkey, China, UNESCO and the US of A. 

The last chapter 'capturing the potential of pluriversal AI ecosystems' summarises the preceding chapters. Discussion is undertaken as to how decolonising AI in in the Industry 4.0 era has implications for CIE.

Overall, the book provides a good summary into the current understanding of AI and education within the CIE context. Discussions on future implications are useful, providing cautions but also possibilities when AI is deployed meaningfully, purposefully, and ethically. 

Ascilite 2014 workshop on learning analytics

Learning analytics workshop with Dr. Leah Macfadyen and Dr. Shane Dawson at Ascilite 2014 held at Otago Polytechnic.

An introductory session. Leah is from university of British Columbia and Shane is from university of South Australia. Handouts from http://bit.ly/11HnjmH

Overall a good overview acknowledging challenges and potentials.

Unedited notes which I will augment when I return to office.

Current LA implementation 

1 extraction and reporting of transactions level data

2 analysis and monitoring of operational performance 

3 what if decision support - such as scenario building

4 predictive modelling and simulation 

5 automatic triggers and alerts - interventions (Goldstein and Katz, 2005)

Most institutions have not progressed beyond stage 1.

Introduce and review field

Cooper 2012 " the process of developing actionable insights through problem definition and the application of statistical analysis.

Solar - measurement collection analysis and reporting of data about learners and their contexts for purposes of understanding and optimising learning and the environments in which learning occurs. 

La draws from business intelligence, web analytics, academic analytics fro 2005, educational data mining 2000 , action analytics 2008 onwards.

Academic analytics on recruitment, retention etc. learning analytics focused more on teaching and learning. 

Drivers of la include socio political, educational and technological. 

Need to move beyond number crunching. EDM laid groundwork for computation and modelling. Interdisciplinary collaboration required technical analytic on statistics, data visualisation and visual analytics, educational data mining, computer science, machine learning, natural language processing, human computer design, etc. 

Systems thinking may be one way to look at la. A system is perceived as elements which are inter connected and inter dependent ISON in Ray and bradbuRy, 2008. 

LA investigates information of educational system information flows and feedbacks in an education system.

Offered examples.

What kind of information? Where students come from, achievement of students, information from student engagement through LMS or similar MOOCs, student monitoring and success tracked by learners, student satisfaction from course evaluations etc., 

Purdue signals project 

Student led system to feedback to students progress through courses. Gathers student demographics. 

University of Maryland Baltimore 'check my activity' tool. 

Austin Peay university's degree compass tool. More of an academic analytic looking at students pass achievements and recommends future courses. 

University of South australia - analytics dashboard. Student learn online engagement ratio. Mainly frequency of access to various Moodle resources and activities. Then calculate at risk on quadrant student current engagement vs student current grade. Low engagement often related to low grades. Link to predicting performance and retention through identifying no risk, low, medium and high. Found intervention with high risk students too late, so shifted intervention to earlier in course, just post orientation. Risks identified as low social economic status, low entry qual. And distance to travel to uni. Students contacted via crm which is used by student services to support students. 

Shane also shared uniSA work on video analytics and annotations. Collaborative lecture annotation system, allows YouTube video to be annotated with student notes. 

Other enterprise level la tools include cavitas, design 2 learn insight, knewton adaptive learning system, blackboard analytics. 

Barriers to adoption  (ferguson et al., 2004)

Review key steps in implementation

Rapid outcomes model approach 

Define and redefines your policy objectives

Map political framework (young and mandizabal, 2009)

Identify key stakeholders 

Identify desired behavioural changes

Develop engagement strategy

Analyse internal capacity to effect change

Establish monitoring and learning frameworks 

Stages include

Define a clear set of overarching policy objectives

Map the policy context 

Identify key stakeholders 

Identify desired changes outcomes

Develop a plan

Ensure engagement team has competencies required to operationise

Establish monitoring or evaluation

What does your institution would like to achieve through implementing LA?

Clarify and prioritise purpose

Plus identify range of skills systems and processes to assist implementation. 

Issues with ethics - purpose, ownership, informed consent, privacy, de-identification, how data handled, who has access, ethics of surveillance, but in parallel with changing attitudes to privacy and self disclosure. Need policies on data governance. 

Workshop closed with session on ethics.

Issues with ethics - purpose, ownership, informed consent, privacy, de-identification, how data handled, who has access, ethics of surveillance, but in parallel with changing attitudes to privacy and self disclosure. Need policies on data governance. Recommended to check slade and prinsloo 2013 for principles of learning analytics and ethical use of student data for la policy (2014) by uk open university.