Academic Technology for Competency-Based Education in Higher Education

Academic Technology for Competency-Based Education in Higher Education

Jonathan M. Kevan (University of Hawai'i at Mānoa, USA) and Christine K. Sorensen Irvine (University of Hawai'i at Mānoa, USA)
DOI: 10.4018/978-1-5225-0932-5.ch006
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As more postsecondary institutions look to competency-based education (CBE) as a model for delivering instruction, technology challenges present major barriers to its adoption. In this chapter, the context of higher education is reviewed in terms of its need for technology to manage the CBE process at scale. The role of technology in delivering CBE is explored followed by a discussion of technologies that hold promise for use in CBE, including vendor-based and open learning management systems (LMS), adaptive technologies, and tools for data analytics. This is followed by a call to re-explore previously proposed (and dismissed) technologies to ensure the ideal path is promoted. Personal learning environments (PLEs) are offered as an example with descriptions of how they may align with the CBE model more effectively in existing academic infrastructure than LMSs. The authors conclude that while a void remains in infrastructure technology to support CBE, growth in CBE appears likely.
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The emphasis of competency-based education (CBE) on learning rather than on time spent in class creates a unique issue for academic infrastructure. The measurement of time, defined as the ‘credit-hour’ in academia, is the basic unit of degree progression driving current academic infrastructure (Silva, White & Toch, 2015). To change this unit is not simple. Unlike time where two students can be checked off for completing the same amount of credit hours, measurement by learning (i.e., measurement of the content that was learned) will always be uniquely contextual to the student. It should be noted that the credit-hour is not only a unit of degree progression, but it is also the cornerstone for the invoicing and scheduling of many services within the higher education business model (Silva, White & Toch, 2015) as well as for the awarding of federal financial aid. To completely remove the credit-hour as the unit by which learning is measured, new technologies are needed not only to support documenting learning progression, but also for business and auxiliary functions such as advising, student registration tracking, financial aid, and faculty work assignments, all of which and more are currently tied to the credit hour as a unit of measure (Bailey, Schneider, Sturgis, & Vander Ark, 2013).

The traditional education model has benefited from a long history of educational technology development that has helped run the business of academia as well as create new educational affordances. One example is the popular Learning Management System (LMS), a technology adopted by nearly 100% of all institutions (Dahlstrom, Brooks, & Bischel, 2014). However, like most academic technology, the majority of LMSs were designed to support the linear and one size fits all content delivery approach, typically implemented in the traditional higher education model (Wilson et al., 2007). The structuring of content this way, as a core function of the LMS, is directly in conflict with the personalized approach of the CBE model, which offers students the ability to self-regulate, enables progression based on mastery, and facilitates personalization of educational content. Support for common CBE practices such as prior learning assessment, the recognition of past learning towards degree progression, and non-traditional learning experiences is generally limited in LMS platforms and often restricts available assessment options, a critical component for measuring student mastery (Book, 2014; Glowa, 2013). Since LMSs support delivery of instruction at the course level, the recognition of prior learning can exempt a student only from an entire course rather than from a specific facet of an overall competency map. Likewise, the tracking of assessments in an LMS is organized at the course level, using course tools (such as a gradebook) and a course designation number versus an individual number for each competency that, upon mastery of each competency, would naturally progress the learner into his or her next required competency. Certainly portions of CBE instruction can leverage current tools, although these existing technologies may ultimately prove too limiting for the CBE model with its potential for education and assessment outside of the traditional course structures. While a small number of competency-centric platforms exist to support the unique attributes of CBE, they are typically comprised of a patch working of individual technologies due to a lack of comprehensive solutions (Le, Wolfe & Steinberg, 2014). This unfortunately raises the barrier to entry for new institutions allowing only those with the technical development capacity and funds to build homegrown solutions.

Key Terms in this Chapter

Learning Analytics: The analysis and application of historical and dynamic student data to improve learning outcomes and academic support.

Adaptive Learning Technologies: Adaptive learning technologies are technologies that have the capability to adapt instruction and access to materials in response to student needs. Examples include intelligent tutoring systems and computerized adaptive testing.

Prior Learning Assessment (PLA): Prior learning assessment includes a variety of approaches for assessing and documenting learning acquired outside of traditional educational contexts and may be used to grant credit, certification or advancement.

Personalized Learning: Personalized learning means designing learning experiences to meet the specific needs of individual students.

Direct Assessment: Direct assessment is a measure of student performance based on a variety of types of actual student work.

Caliper Analytics: Interoperable learning data standard lead by IMS Global and affiliates designed to streamline and integrate future educational technologies.

Experience API (xAPI): Constructivist-based specification for the structure, collection and storage of learning data across various technologies and learning experiences.

Learning Management Systems: Learning management systems include software applications that allow educational institutions to organize and deliver content to learners.

Self-Regulated Learning: The act of students structuring, planning, and implementing their own process towards the completion of prescribed and self-identified learning objectives.

Council for Adult and Experiential Learning (CAEL): CAEL is a non-profit organization historically committed to supporting opportunities for adults to learn, including through such mechanisms as prior learning assessment, experiential credit, and competency-based education.

Competency: Competency is the ability to demonstrate a specified level of knowledge or skill.

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