Innovative technologies are transforming the landscape of education. While there is great enthusiasm for and considerable investment in cutting-edge EdTech, there still remains the pressing issue of promoting equity in education. Although digital programs and applications have huge potential for increasing the availability and affordability of educational resources, mounting evidence would suggest that there is a widening gap in student performance and this is closely linked to what is referred to as the ‘second digital divide’.
What is the latest research on the equity implications of technology? How can online education platforms best serve the interests of students and teachers in economically challenged areas? In what ways can emerging tools democratize education and improve learning outcomes? In this selection of articles, specialists in this field bring their own insights and perspectives to explore the key issues at the intersection of technology, equity and learning.
The Second Digital Divide: Privileged Usage of Educational Technologies
Dr. Justin Reich
Assistant Professor, MIT
Harnessing Technology to Bridge Gaps in Education
Mr. Arnold (Cairui) Fu
Founder, Chairman and CEO, Hujiang EdTech
Using Data to Drive Outcomes in Education: Getting the Right ‘Recipe’
Director, Green Shoots
There has always been an ambivalent attitude to the large-scale use of especially digital data in education, which can oscillate between two equally unhelpful extremes. On one side is the view that given the immense detail of digital learner data and the complexity of processing algorithms available, learner tracking and guidance should be automated, albeit by clever machines that can learn as they go along. The other view is that there is nothing that could ever compare with the insights of the teacher in their classroom, so the teacher’s view is the only view that should ever be counted. As always in scenarios with two extremes, the approach to the use of data that can yield the most impact (improved learning outcomes) is somewhere in the middle. The problem has been in locating that ‘middle’ for both schools and education districts and systems.
So what is the elusive recipe for the successful use of data to drive outcomes in schools? I would never lay claim to having THE recipe for success, but I will venture to suggest some possible vital ingredients below. Bearing in mind that all the best cooks will say that the success of the cake depends largely on the quality of the ingredients!
- Individualized, detailed, diverse and real-time (where possible) digital learner data is now possible and readily available thanks to the deployment of EdTech on a large scale in classrooms.
- This essential ingredient must be complemented by non-digital data sets. They encompass information such as contextual issues, human insights and observations of both learners and learning.
One of the issues of data use within education is the expectation, found at all levels, that merely assembling the ingredients (data sets) will give you the perfect cake (improved outcomes). As in the world of baking, a multi-step process is needed to achieve results. Again this list is NOT definitive but just a sketch of the steps to consider.
- Key metrics need to be decided upon for the analysis and outcome indicators – What is the question that YOU are asking? What do YOU need to know from the data? Prioritize and target the analysis provided! Far too often valuable trends and warning signs are drowned in a sea of unnecessary information.
- Data literacy should be addressed. Don’t assume that everyone is a data savant and knows how to interpret the huge quantity of analysis available. What am I looking for? What should I expect to recognize if there is progress? What are the warning signs I should look out for? Interpretation is often the hardest aspect to grasp. This is the vital link between a point on a graph and what it is showing about what is actually happening within classroom.
- Collaborative reflections. We often end up dealing with our particular data in isolation, especially when it is generated electronically. There is such a benefit in sharing our interpretations of the analysis, comparing trends and issues and collaborating on possible solutions.
- Learner engagement – data is not just about learners, it should be for them too! We are missing out on an important step if learners are not part of this reflective process. We are hoping to enable learners to analyze and respond dynamically to situations – this is one way to cultivate this type of thinking.