Published on 2017/03/10
The EvoLLLution | Big Data, Big Opportunity, Big Challenge
Leveraging Big Data can have a transformational effect on the institutional ability to serve students while also helping leaders and staff to be more effective and impactful without overloading them.

Continuing educators (CE) were the first to offer courses, certificate programs and degrees in digital analytics and in the related fields of predictive analytics, Big Data and the Internet of Things. Unfortunately, educators and administrators alike have been less aggressive in leveraging data analytics to enhance their own offerings and operations. However, there are emerging pathways for CE administrators to use these new technologies.

An understanding of the concepts, domains of usage, and ways in which digital analytics can be used in CE are essential for all leaders.

Definitions and Domains of Use

Digital analytics is a general term to describe the use and analysis of large data sets. It has been defined by the EAB as “the process of extracting, organizing, and modeling data to transform it into information for decision-making processes.” This definition encompasses data science and analytics of all kinds, including business and learning analytics.

Learning analytics are a natural outcome of several forces. “The proliferation of data mining software and developments in online education, mobile learning, and learning management systems are coalescing toward learning environments that leverage analytics and visualization software to portray learning data in a multidimensional and portable manner.” (A. Becker et al., 2017).

The common feature of digital analytics is the enormous size of the data sets that are being generated. For instance, Google Books and other utilities have digitized the contents of over 62 million books, according to the EAB. The Internet of Things (IoT) is important because this technology produces large data sets in almost every field, including learning. IoT “consists in the network of objects embedded with software, sensors or connectivity features that are able to connect to the internet and to interoperate.” (C. Paini, F. Cardinali, 2017). It’s estimated that by 2020 over 30 billion devices will be wirelessly connected to the internet. (C. Paini, F. Cardinali, 2017).The enormous amount of data created through the IoT and the digitization of text and images will have to be analyzed to yield information for improving our lives.

Digital analytics and Big Data are being used in education in several domains. The most prominent use is to understand and improve learning. This domain breaks down into four sub-domains:

  1. Learner interventions
  2. Adaptive learning
  3. Instructional design improvements
  4. Marketing

Learning Interventions

Universities are increasingly able to identify “at-risk” learners and then devise counseling and supplemental learning activities to help them be successful. For instance, at several universities it was discovered that graduation rates dropped significantly with students who were getting less than a B in a major’s foundational courses. Large foundations are supporting these efforts and universities are coming together to use learning analytics to improve graduation rates. The Lumina Foundation has adopted learning analytics as a topic of interest and the members of the University Innovation Alliance, composed of ten universities, are collaborating to use learning analytics. (J.B. Treaster, 2017).

Implications for CE Administrators: Learning interventions are more applicable to full degree programs where student retention is often correlated with learning difficulties. Research has shown that retention of students in non-degree programs is primarily due to the fact that for adult learners “life gets in the way.” Most CE learners have at least one degree. These students are therefore relatively sophisticated, qualified learners that are engaged in much shorter learning projects where retention is not so big an issue. Learning analytics can still be useful, but they have a different focus in CE.

Adaptive Learning

Closely linked to learning analytics, adaptive learning “refers to the technologies monitoring student progress—using data to modify instruction at any time. Adaptive learning technologies, according to EDUCAUSE, dynamically adjusts to the level or type of course content based on an individual’s abilities or skill attainment.” (A. Becker et al., 2017). Although adaptive learning is not widely utilized now, it has proven to be highly successful.

Implications for CE administrators: Adaptive learning is very expensive to produce, requires a technological infrastructure usually lacking in higher education institutions, and is applicable in only a few highly structured fields of inquiry (math and science). These restraints usually confine CE users to third-party adaptive learning tools such as ALECS for math and chemistry.

Instructional Design Improvements

Learning analytics are increasingly being used to improve learning treatments in a continuous improvement cycle. The cycle begins with a particular course instance and then uses learning analytics to improve the course for the next iterations. This type of learning analytics is made possible by the data being generated by online education learning management systems, which can generate detailed data about the learning treatment. For instance, outcome data can analyze the effectiveness of test items, identifying those questions that many students get wrong. This data provides an opportunity to either adjust the item to be clearer or to modify the course to cover the material more thoroughly.

Implications for CE Administrators: Some form of continuous improvement, particularly in online formats, is essential as the quality of the course increases due to professional instructional design. Investments in instructional design are too expensive unless they can be carried over into more than one course offering. Once there is a sustaining instructional design underpinning, it makes sense to use data from learners to improve the quality of the design. A further implication is that there needs to be some form of content management so that improvements can be made easily.

Marketing

Many continuing educators are now using advanced techniques and digital analytics to retain students and attract new audiences. These techniques and supporting technologies are becoming ever more sophisticated and complex. For instance, many universities now use Burning Glass, a company that gathers and analyzes data from hundreds of thousands of job listings, to align curriculum development with workforce needs and to identify local CE demand niches. The EAB offers its members access (at a price) to a database of over 200 million Americans. One application of the analytics coming out of the EAB database is the opportunity to match the profile of a particular CE unit’s current audience with those people who are not students, but have the same characteristics.

Implications for CE administrators: Because of the increased complexity and sophistication of marketing-related digital analytics, CE administrators will have to rely on external expertise to bring marketing efforts up to date. Analytics will concentrate mainly on lead generation and lead nurturing, most likely requiring third-party expertise.

Conclusion

Digital analytics is clearly a disruptive technology that CE administrators will need to understand and use aggressively to maintain a competitive advantage, particularly as competition increases from the non-academic world. It’s ironic that the technology created and advanced by universities may be the cause of our ultimate relegation to minor players in the CE industry. We can’t let this happen, but must move quickly to stay ahead of and minimize the disruption.

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References

Adams Becker, S., Cummins, M., Davis, A., Freeman, A., Hall Giesinger, C., and Ananthanarayanan, V. (2017). NMC Horizon Report: 2017 Higher Education Edition. Austin, TX: The New Media Consortium. Retrieved from http://cdn.nmc.org/media/2017-nmc-horizon-report-he-EN.pdf.

EAB, COE Forum, Creative Disruption, PowerPoint Presentation, 2017

Paini, M., Cardinali, F. (2017). The Learning Analytics Community Exchange, The LACE LAW Manifesto. Bolton, UK: Learning Analytics Review. Retrieved from http://www.laceproject.eu/publications/LACE-LAW-manifesto.pdf.

Treaster, J. B. (2017, February 5). “Grades that foretell your future.” The New York Times, p.4,5.

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