Competency-Based Education in Higher Education

Competency-Based Education in Higher Education

Christine K. Sorensen Irvine (University of Hawai'i at Mānoa, USA) and Jonathan M. Kevan (University of Hawai'i at Mānoa, USA)
DOI: 10.4018/978-1-5225-0932-5.ch001
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Abstract

This chapter summarizes the background and current status of competency-based education (CBE) in higher education. After briefly reviewing the history and current state of CBE in higher education, the authors address the more recent uptick in CBE options in higher education as well as potential drivers of demand, including changing demographics, demands from the public and employers for reduced costs and evidence of outcomes, and rapidly evolving technologies. The chapter includes examples of CBE programs in operation or development at selected post-secondary institutions and concludes with a brief look at barriers and challenges higher education faces in implementing CBS as well as possible opportunities for the future.
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History

Competency-based Education (CBE), or at least a form of it, may be one of the oldest instructional models. Progression in training and social status through evidence of “competency” can be traced back through a number of societies, including professional organization-like guilds during the medieval period (Trueman, n.d.). More recently, as Klein-Collins (2013) points out, “An intensive focus on what students know and can do rather than on what is taught, for instance, is a hallmark of CBE programs going back at least four decades (p.4).” The Council for Adult and Experiential Learning (CAEL) has been an advocate for CBE for over 40 years with a belief that learning should be recognized both in and outside of the classroom (CAEL, 2013). CAEL was also an early leader in prior-learning assessment (PLA) which, like CBE, recognizes that how and when something is learned is not as important as whether the learning has occurred and the individual can demonstrate competency in the particular subject matter (CAEL, 2013). “Prior learning” is learning that is typically acquired outside the traditional academic setting and PLA is the process by which that learning is assessed and evaluated to grant credit, certification, or advanced standing (CAEL, 2013). Some colleges experimenting with CBE incorporate it in conjunction with PLA. Others, while not using traditional PLA approaches, recognize prior learning by embedding its assessment within the CBE program (Educause, 2015).

Key Terms in this Chapter

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

Degree Qualifications Profile (DQP): The DQP is a framework developed with support from the Lumina Foundation that outlines learning outcomes in five categories appropriate for students at different levels of postsecondary education.

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

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

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

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.

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.

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.

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

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

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