OVER the last few months, learning institutions have been slowly coming out of an emergency remote teaching (ERT) environment as the Covid-19 pandemic eased. There seems to be a refocused vision of how to continue using emerging technologies for a more impactful teaching and learning process.
During the pandemic, many educational institutions transformed teaching and learning from an environment that was highly face to face to one that was fully online.
While there was insufficient time to plan and execute technology-enhanced learning using time-tested learning design principles back then, we should now go back to the drawing board and find out how our learners would like to continue their learning and how best we can meet their requirements and expectations.
In moving forward and planning for more intuitive teaching and learning practices, we should take the following trends, as indicated in the 2021 EDUCAUSE Horizon Report: Teaching and Learning Edition, into consideration.
Artificial intelligence (AI)
The role of AI in teaching and learning, as reiterated by the Beijing Consensus on Artificial Intelligence and Education (BCAIE), is to systematically innovate education and to accelerate the delivery of open and flexible education systems, thus enabling equitable and quality lifelong learning opportunities for all. Its role in transforming teaching and learning is becoming more crucial, especially related to the learning management systems, assessment processes, student educational experiences, and admissions.
In some recent discussions on AI and higher education, two key points have emerged.
First, the question of whether AI can be used to address challenges in teaching and learning, as well as successful learner experience.
Second, the suggestion to meticulously rethink the impact of the existing curriculum and if it will serve the new generation of digital learners.
The education sector can actively participate in these disruptions by taking an active lead in capitalising on AI to affect curriculum and pedagogical designs.
Increasing amounts of digital teaching and learning data have become available and tapping into this huge data resource will enable researchers in education to better design AI-supported ecosystems. To do this, it is important to reflect on what educators’ challenges are, and how AI and emerging technologies can be harnessed to positively disrupt education to aid educators and be of high assistance to learners.
The following are some innovative suggestions from different research studies, publications and white papers on the use of AI to transform teaching and learning:
> Using AI to recognise teaching and learning patterns to enable high-quality support to educators, especially to release them of routines such as administrative responsibilities, teaching basic content, and rote learning activities;
> Using AI to create new forms of learning to suit the needs of future-ready learners. To this end, AI and emerging technologies need to be designed and programmed to capture the available technology-based teaching and learning algorithms; > Capitalising on AI to determine how different learning pathways (formal, informal and non-formal) can be mapped to core learning outcomes, and how this can then be analysed to form new curriculum needs;
> Using AI to determine the impact of collaborative learning and how teachers can be supported in this highly cognitive activity, which consumes teachers’ teaching and planning time;
> Developing AI applications to empower teachers towards the adoption of more inclusive pedagogies, to help teachers detect learning deficiencies, diagnose varied learning challenges faced by learners and suggest solutions; and
> Developing human-machine collaborative AI tools to enhance the quality of subject-specific and interdisciplinary learning, especially in courses related to science, technology, engineering, the arts, and mathematics (STEAM).
Learning analytics (LA)
LA involves tracking, analysing and interpreting student data related to learning behaviours, particularly those that are digitally related.
An institution’s learning management system (LMS) is the main conduit to provide LA as this is where data related to student and faculty interactions is stored or occurs most.
According to a review of the United Kingdom and international practices on LA in higher education institutions, the following are some of LA’s significant contributions:
> A tool for quality assurance and improvement
LA can be used at the individual level by an academic to provide a more customised and fulfilling learning experience to learners.
At the university level, it could be used to transform the curriculum. In the long run, data from LA may assist a university to create better teaching and learning policies, pedagogical frameworks, and assessment methods and processes.
At the national level, LA could assist in better compliance and quality assurance, especially with regard to the Malaysian Qualifications Agency’s requirements.
It could also provide analytical insights for future educational transformation nationwide.
> A tool for boosting retention rates
Data from LA could assist in intercepting at-risk students earlier in the programme than would otherwise be possible as LA captures digital traces of learning in the LMS.
Although most educators may have incorporated the universal design for learning principles into their online learning designs, as a method to boost retention, there is still a high potential for improving the design of learning based on data from LA.
Retaining online students who are geographically displaced, using various learning modes (hybrid and blended), and managing different time zones can be rather demanding.
Accordingly, LA can be used powerfully to identify at-risk students, and develop strategies to deliver timely, effective and meaningful support to these students.
> A tool to provide a more personalised learning experience
Personalised learning reframes learning experiences by incorporating the most appropriate approaches and content for an individual student.
The beauty of having access to LA data is that it allows for adaptive learning experiences to be built into the system to enable learners with different learning issues to have a better learning experience.
One of the keys to implementing a quality personalised learning approach – one that is more likely to result in improved student learning outcomes – is a focus on available analytics.
In other words, one needs to understand LA to personalise and adapt learning to ensure a more successful learning experience.
As personalised learning expands in the coming years, so will the demand for LA to improve the quality and efficacy of the learning designs.
As the number of students with access and skills to Internet-connected personal devices is increasing, and schools are opening their networks to support them, it is important for educators to be mindful of the capabilities of LA to support personalised learning.
To conclude, it is both imperative and timely for education stakeholders to consider harnessing new technologies such as AI and LA to transform teaching and learning, thus enabling impactful learning experiences that matter most to learners.
Dr Abtar Kaur is a professor of innovative digital learning, the director of Digital Learning Hub, and the Unesco chair – Use of Innovative Technologies to Enhance Quality of Teaching – at Asia Pacific University of Technology & Innovation (APU). She obtained a Master of Science in Instructional Design, Development and Evaluation from Syracuse University in the United States, and a PhD in Web-Based Learning from Universiti Malaya. Abtar did her post-doctoral research (Fulbright) at Indiana University, the US.
The views expressed here are the writer’s own.