To engage students is to help maximise their potential 

Digital transformation is a wide-stretching topic, and one where the picture vastly differs depending on the institution. Engagement analytics is a seemingly small component that fits into the larger digital picture but is an essential tool helping universities to improve student success, enhance teaching and learning, inform curriculum design and create the best student experience. To engage students is to help maximise their potential, but to achieve this, institutions need to ensure they are focused on the outcomes of their students. 

There are many universities who have explored educational technologies to enhance in-person learning, yet there is still a way to go in terms of adopting a digital mindset with data that can result in achieving the best result possible. 

Using the data we already have 

The data that already exists in a university and is created every single day is the most accurate evidence of how students are receiving and engaging with their studies. It tells us where they may be struggling, and where further support could be needed. While this wealth of data is often overwhelming, it can be streamlined into accessible information which can feed into a wider engagement strategy. 

One university that is exploring the strategic value of data-informed decision making is The University of Essex. It is been adapting its approach in line with its education strategy which focuses on ensuring success and good wellbeing for students. Implementing learning analytics through StREAM, Solutionpath’s Student Engagement Analytics Platform, – known as LEAP (Learner Engagement Analytics Portal) at the institution, it can now gather data on the engagement and attendance of its students and support the wider strategic development of the education offered, as well as offer help and guidance for those at risk of early withdrawal. Richard Stock, Academic Registrar at the University of Essex, says that data is “playing an increasingly important part in our ambitions” and explains that part of the University’s education strategy is “the responsible use of data as a tool for development, wellbeing and success” for students. The University has gained a wealth of insight that has been pivotal for designing student support to tackle critical spots throughout the students’ learning journey, while also adhering to its approach to induction and transition activities to increase retention in the first few months of a course.

Start with the future

Starting small and progressing at a pace that suits your strategy can enable more effective outcomes. However, such strategies need to be suitable for the long-term and grow with the ever-changing landscape of technology.

Teesside University started integrating engagement analytics as the Covid lockdown began and quickly realised the important role analytics play in the wider transformation picture, enabling the University to identify students who were engaging with their studies. Mark Simpson, Pro Vice-Chancellor at Teesside University, says “Our future-facing learning strategy has been used to drive significant change within our organisation. This includes how we will help our students to become globally connected, ethically engaged and digital empowered.” As illustrated by Simpson, a future-focused stance adopted by the institution can accelerate the positive change needed to help students feel as if they are getting the most out of all aspects of their education. Teesside has now progressed to revising its approach to personal tutoring by creating a central student success team, allowing the University to effectively identify issues among students and provide an intervention to support. 

Developing a transparent analytics environment  

You can’t have successful student analytics and future-thinking strategies without deep consideration of who makes up your student population. Data is more than technology and figures – it’s people and culture. This means developing a transparent analytics environment where students have a clear understanding of how their data will be used solely for the purposes of supporting their academic success and can access the same data as staff, giving them the freedom to use technologies that will help them gain perspective on their own learning journey.

Engagement analytics is a vital aspect of building a solid relationship between the staff providing intervention support and students, who can use this data to create a detailed picture of an individual student and work in collaboration to create a unique roadmap of development. Andy Ramsden, Director of Technology Enhanced Learning & Teaching and Learning Analytics at The University of Law, highlights that “A key pillar within data strategy is people development”. In other words, all data analysis is set out to point back to how best to maximise student development.

Students need to be involved in the digital transformation of their own university, as the deployment and development of technology is primarily for the benefit of themselves. This means having an engagement analytics platform that clearly outlines their behaviour and progress in a simple dashboard with easy-to-understand measurement insights. Helping students grasp the data behind their engagement allows for more meaningful conversations to take place with their tutors and empower them to take their next steps. Ramsden further notes “For all people, staff and students, to take ownership of the data they need to be comfortable with seeing stories through the data.” The data needs to be presented in a way that is more than just numbers, but a detailed report that strongly ties back into the student’s own life.

By having students fully on-board with their university’s digital strategy, including the use of their data, a culture of transparency is established, allowing valuable conversations to occur and real change to be seen.

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It turns out that making machines appear to be human actually takes a remarkable number of people to create the data sources that drive the AI algorithms and fuel the analytics used in decision making. And as Dzieza says:

“You might miss this if you believe AI is a brilliant, thinking machine. But if you pull back the curtain even a little, it looks more familiar, the latest iteration of a particularly Silicon Valley division of labor, in which the futuristic gleam of new technologies hides a sprawling manufacturing apparatus and the people who make it run.”

The article is a wake-up call and a reminder that in times of massive technological change, people suffer. Sometimes a lot of people. It’s a troubling aspect of the digital transformation of business and society that all of us must face up to. With the recent acceleration of AI adoption, taking time to reflect on the role of data in driving AI and the dilemmas raised by advances in this technology is essential.

The humans in the loop

It is worth repeating that the secret to AI is people: Humans and machines working together and making the most of one another. However, we also need to beware of the potential negative impacts of this relationship. With increasing adoption of AI technologies, it is becoming clear that such interaction comes with a variety of troubling human costs:

Job Displacement and Reskilling: Automation driven by AI can lead to the displacement of certain jobs, particularly those involving repetitive and routine tasks. While AI creates new job opportunities in areas such as AI development, data analysis, and AI ethics, the transition is hard on individuals whose skills become obsolete. Many people will struggle to adjust.

Bias and Fairness Concerns: Biases and influences from many areas place pressure on the ways that AI systems are built and evolve. This can exacerbate existing inequalities and lead to discriminatory outcomes in areas like hiring, lending, and law enforcement.

Privacy and Security: AI technologies, particularly in the collection and analysis of personal data, raise significant privacy concerns. The extensive collection and analysis of personal data for profiling and decision-making can erode individual privacy rights and lead to unintended consequences, such as inappropriate monitoring and profiling.

Ethical Dilemmas: Deploying AI systems brings many kinds of ethical dilemmas in their decision-making processes. For instance, there are well known case studies that highlight the issues faced in self-driving cars as they make split-second decisions that involve weighing different priorities to choose a “least bad action”. Determining the “right” course of action in such situations is complex, ambiguous, and open to ethical challenges.

Depersonalization of Customer Service: The use of AI-powered chatbots and automated customer service systems can result in a depersonalized customer experience. While these technologies offer efficiency, they can be viewed as “dehumanizing” and lack the empathy and nuanced understanding that human interactions provide.

Mental Health Impact: Constant connectivity, social media algorithms, and AI-driven content recommendations have been linked to negative impacts on mental health. These technologies can contribute to feelings of social isolation, lack of self-worth, and addiction.

Loss of Human Judgment: Overreliance on AI systems can lead to a decline in human judgment and critical thinking. From Nicholas Carr’s warnings about “Google making us stupid” to more recent comments on the need for “explainable AI”, blindly following technology-driven AI recommendations reduces individual participation and understanding of complex situations, and removes the need for people to learn how decisions are made.

It’s always been about data

Beneath each of these human dilemmas is a story about data. The way that AI systems procure, manage, and apply data is a determining factor in the human-machine relationship. This shows its head most obviously in the way AI systems are trained.

The quality and effectiveness of AI systems are intricately tied to the source and calibre of their training data. Good training data is the foundation upon which AI models are built, shaping their capabilities, accuracy, and real-world applicability. It serves as the essential raw material that allows AI algorithms to recognize patterns, make predictions, and perform tasks with accuracy and relevance.

In a rapidly advancing AI landscape, the role of high-quality training data cannot be overstated. It is the cornerstone on which the entire AI infrastructure rests. Investments in obtaining and maintaining good training data pay off by yielding AI systems that provide accurate, reliable, and valuable insights, ultimately determining their success and impact across various industries and applications.

Training data essentially guides AI models in understanding the complexities of the world. When the data is comprehensive, diverse, and representative, the AI system can generalize from the examples it has seen during training to make informed decisions on new, unseen data. This capacity for generalization is what makes AI systems valuable and adaptable to different scenarios. Conversely, poor-quality or biased training data can lead to skewed outcomes and unreliable predictions.

Getting good training data can be costly. And is harder to come by than many people think. Ensuring good training data involves meticulous curation, validation, and augmentation. Data then needs to be cleaned, verified, and balanced to mitigate biases and inaccuracies. Moreover, the continuous refinement of training data is vital to keep AI models up-to-date and relevant as trends and contexts evolve. As Dzeiza’s reminds us, this takes people — a lot of people.

The latest AI advances illustrate just how much data is required. GPT-3.5, the LLM underlying OpenAI’s ChatGPT was estimated to have been trained on 570GB of text data from the internet, which OpenAI says included books, articles, websites, and social media. The importance of good training data is particularly evident in supervised learning, where AI models learn from labelled examples. If the labels are incorrect or inconsistent, the AI’s understanding becomes flawed. In addition, the absence of specific examples can hinder the AI’s ability to grasp the full scope of a task, limiting its performance.

Let data be your guide

Regardless of the collection or generation process, the quality and volume of training data are critical factors that significantly influence the performance and reliability of AI models. There are three major concerns related to these aspects that we all must try to avoid.

The first is bias and unfairness. One of the foremost concerns is the presence of bias in training data. If the training data is biased, the AI model will learn and perpetuate those biases, potentially leading to discriminatory or unfair outcomes. Biases can arise from historical inequalities present in the data or from sampling biases that don’t accurately represent the diversity of the real world. For instance, if a facial recognition system is trained predominantly on one demographic group, it might perform poorly on other groups, exacerbating existing societal biases. Ensuring a diverse and representative dataset is crucial to mitigate bias and promote fairness.

The second is Data Quality and Labelling. The accuracy and reliability of the training data labels are paramount. Incorrectly labelled data or noisy data can mislead the AI model and result in poor performance. In supervised learning, where models learn from labelled examples, even a small percentage of mislabelled data can have a significant negative impact. Maintaining data quality requires careful validation, error correction, and constant monitoring. In domains like medical diagnosis or autonomous driving, unreliable labels can lead to serious consequences, making data quality a critical concern.

The third is Data Volume and Generalization. The volume of training data plays a crucial role in the generalization ability of AI models. Too little data might result in overfitting, where the model memorizes the training data but fails to perform well on new, unseen data. On the other hand, insufficient data can limit the model’s ability to grasp the complexities of a task. While deep learning models thrive on large datasets, collecting and annotating massive amounts of data can be time-consuming and resource-intensive.

Addressing these concerns requires a multi-faceted approach. It involves careful data collection, pre-processing, and augmentation to ensure a diverse and representative dataset. Implementing techniques to detect and mitigate bias, both in data collection and model training, is crucial. Data quality control measures, such as crowd-sourced validation or expert reviews, can help maintain accurate labels. Additionally, techniques like transfer learning can enable models to leverage knowledge from one domain to improve performance in another, even when data is limited.

Keeping it real

A recent eye-opening article by Josh Dzieza in The Verge shatters the illusion of AI’s autonomous brilliance. Behind the scenes, the success of AI hinges on something often overlooked: high-quality training data. This is often expensive and difficult to create and manage, and requires a lot of people doing challenging work. In this era of rapid technological change, it’s crucial to recognize this as part of the interplay between humans and AI. While AI offers tremendous benefits, it also presents significant challenges that demand our attention. Job displacement, bias, privacy concerns, and ethical dilemmas are real issues that need careful consideration.

But above all, quality training data is the bedrock of AI’s capabilities. It’s the raw material that shapes AI models’ accuracy, adaptability, and real-world performance. As we embrace AI’s potential, it is essential that we emphasize the importance of meticulous data curation, diverse representation, and bias mitigation. By doing so, we can pave the way for AI systems that enhance our lives while upholding ethical and societal standards.

Originally posted here

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