The opportunities for humans to contribute to the work of the world are changing rapidly. Businesses growing to take advantage of these opportunities need workers with new skills. Programmers, data scientists, web developers, and leadership positions are hiring, but there are not enough folks with the right skills to fill the need. This is true of many industries.
Education is expensive. Traditional teachers have to be multi-talented, high educated, passionate, and hard-working. Teaching and assessing are done manually and at considerable expense of time and money. However, we live in a magical age where data driven, accessible, personalized, and effective virtual education is possible. But how does an enterprise achieve those lofty goals? Data and data science are the answers
I jumped into the work of learning analytics with a project to predict course outcomes by online study patterns in user data and had a hard time finding data on learning and learning platform interaction patterns. Fortunately I found this dataset from Open University which has been a well-used source of study.
Traditional educational research is hard. Kids are squirrely and inconsistent. Classrooms are unique and external variables abound making controlled experimentation difficult. Group sizes are small and variables differ greatly between classrooms. The data is labor intensive to collect and often subjective.
Educational technologies and distance learning providers have opportunities for rigorous research with hard numbers on large samples of students. Every interaction with the computer can be tracked and anonymized to find correlations between product features, user behaviors and learning outcomes to iteratively redevelop course standards and interactive tools. The data is hypothetically available, and with many interactive cloud-based learning platforms being adopted by schools, the data may begin to span many years. Researchers can begin to model correlations between product features years ago to later outcomes, conducting cheaper longitudinal learning research.
If data is collected ethically and anonymously, according to FERPA guidelines, the community of online educational institutions have the opportunity to share research, data, and data drive practices for learning analytics and modeling.
Data from online learning gathered by online learning providers should be, as a norm, anonymized and provided as public datasets. These datasets can be a source of important and generalizable learning research by academic, professional, and amateur data scientists. Modern data science tools make data and analysis much more transparent than they used to be.
If marketable skills can be efficiently built in a diversity of learners, as is the mission of companies like Coursera and Udemy, we can re-employ millions of people into higher paying and highly needed jobs or business ventures.
Open source online learning datasets can be a source of important new research and advance the science of learning.