Competency Classification Index (CCI): A Taxonomy of the Knowledge, Skills, and Attitudes Achieved by Learners Abstract The Competency Classification Index (CCI) will radically simplify the assessment, transcription, and workforce connections of learning achieved in formal and informal contexts. Inspired by medical coding and classification, CCI will establish a national taxonomy of competency codes by level and specialization allowing the articulation and seamless transfer of knowledge, skills, and attitudes achieved in both formal and informal settings. This provides an opportunity for national comparability as well as the collection, classification and presentation of competencies. Implications include improved transfer of both competency­based and traditional time­based curricula, mechanisms for articulation of competencies gained through work experience, and transparent articulation of critical competencies necessary for workforce development and delivery. Need While there are many potential benefits of competency­based education, several significant issues continue to persist. These include, difficulty in traditional credit hour to competency transcription, challenges assessing competencies across institutions, and an inability for the workforce to identify discrete competencies in traditional degree transcripts. The goal of the CCI is to meet the needs of numerous stakeholders by standardizing the articulation of learning by an individual regardless of age cohort, levels of formal education completed, and credentials earned. Value A competency classification index has great value and substantial benefits for various stakeholders including students, educators, accreditors, administrators, and employers. Relevance to Stakeholders Educational stakeholders, both internal and external to P­20 institutions, could benefit as a coding system has the potential to disaggregate learning from time ­ and is consistent with competency­based learning initiatives. This creates new opportunities for assessment and accountability. Likewise, a coding system can augment the data available in enterprise systems without necessitating wholesale change in curricular mechanisms. Like any other implementation, adoption requires significant staff resources (time and system configuration) but limited resources for ongoing use. The potential efficiencies across the educational landscape are staggering. CCI creates the standardization sought by overseeing business operations and enterprise systems (e.g., AACRAO, Ellucian Banner), a structure for program assessment (e.g., institutional accreditors ­ SACSCOC & professional accreditors ­ AACSB), and additional data points that could develop into criteria for financial aid disbursement, along with greater learning analytics. Employers benefit because a coding system creates a bridge between competencies learned and competencies needed in the workplace. A competency classification system would allow business and industry to articulate the knowledge, skills, and attitudes required for specific jobs at a granular level. This will allow for alignments between curricula and the positions available in the workforce. It will clarify career pathways and has the potential to align educational experiences with current and future competency models. Finally, a competency classification index can streamline the inclusion of non­collegiate or non­credit learning within learner profiles, as the CCI could augment or exist independent of collegiate credit hours. This further facilitates initiatives such as alternative credentialing, articulating continued professional development as sub­competencies, and creating a bridge between college credit and prior learning assessment efforts. Impact on Underserved Learner Populations The CCI will increase the ease in which learners, particularly adult learners, can have their knowledge, skills, and attitudes acknowledged by institutions in their pursuit of academic credentials. Subsequently, CCI will greatly impact underserved learning populations who have gained competencies through non­traditional learning experiences such as in the military, employment, certifications, and professional development. Additionally, students pursuing portfolio­based prior learning assessment (PLA) would realize benefits. For instance, students could use a syllabus to understand which specific competencies are achieved in a course, then identify the specific competencies they have achieved through other college­level learning experiences using their portfolio. Stakeholders (students, institutions, employers and the community) will find a coding and classification system relevant as it creates a framework that supports a system; a system that produces highly qualified individuals for workforce needs. The development and validation of a coding system presents the opportunity for P­20 and workforce collaborations on a national level. Description The CCI project would be undertaken by various entities with the goal of developing a taxonomy that standardizes the knowledge, skills, and attitudes achieved by learners in P­20 and other learning environments. The goal of this narrative is to articulate the need for such a classification index and to provide a framework for developing a coding schema as well as suggesting the stakeholders that should ultimately be responsible for undertaking this task. A non­profit entity would presumably be necessary to conduct this work. Proposed bylaws for such a non­profit entity can be found ​
here​
. Initially, a modest membership fee would be required for participation in the non­profit entity, with membership providing voting and participation rights in the development of the classification index. A basic income and expense table is included below. This project may eventually lead to apps and web solutions for users. As such, external funding from entities that support such efforts (e.g., Lumina Foundations and Educause) has been included in the three­year forecast. Because this is a collaboration among educational providers, employers, governmental, as well as non­governmental agencies, limited staffing would be required, and could be arranged through consulting if membership fees were available to offset these costs. Table 1: Projected Three­Year Profit & Loss Statement for Non­Profit Entity Membership Fees Members Income Income from membership dues Other forms of income Grants Meeting Registrations Miscellaneous Total Income Expenses Administrative Fees Year 1 499.00 10 Year 2 499.00 20 Year 3 499.00 30 4,990.00 4,990.00 9,980.00 50,000.00 4,000.00 63,980.00 14,970.00 100,000.00 6,000.00 120,970.00 500.00 500.00 500.00 4,140.00 1,000.00 360.00 49,500.00 500.00 7,980.00 63,980.00 0.00 6,520.00 1,000.00 480.00 99,000.00 1,000.00 12,470.00 120,970.00 0.00 Meeting Space & Hospitality 3,370.00 Technology Teleconferencing software 1,000.00 Website Hosting 120.00 Application Development Application Hosting Marketing 0.00 Total Expenses 4,920.00 Net Revenue 0.00 Data Driven Design CCI seeks to solve several issues that exist within education but specifically addresses two problems centered around learners, the talent gap and transfer articulation. By seeking to solve such issues, CCI’s concept, development, and implementation will involve numerous data­driven decisions. At a fundamental level, medical diagnostic coding is used as a proof of concept. Much has been written in recent years regarding the talent gap, as there is evidence that suggests college graduates leave higher education with credentials yet lack workforce skills (Selingo, 2015). With this in mind, the CCI could facilitate conversations and collaborations among P­20 educators and employers as it creates a common nomenclature around inputs and outputs of educational systems and workforce development needs. Transfer students often realize a transfer­penalty as credits are often lost, or accepted as elective credits rather than credits that meet degree­requirements (Bidwell, 2016). The CCI facilitates improved transfer because it provides transparency of achieved learning. If competencies are articulated in both the previous coursework and destination degree program, students may find that more of their previous learning will count towards a future credential. Technology Enabled Implementation Based on the conceptual framework behind medical diagnostic codes and associated medical classification, the CCI codifies learning into classifications of cognitive, skill, and affective competencies by depth (e.g., k­12, undergraduate, graduate, professional development) and breadth (e.g., employer competencies, profession­specific competencies). As with the medical International Statistical Classification of Diseases (ICD), the CCI allows for comparability of competencies within and between educational experiences providing a seamless articulation of competencies across institutions, accreditors, and employers. The transparency of actual learned competencies, as opposed to generally assumed competencies inferred by credentials, will meet the needs of various stakeholders. The proposed classification index includes a code schema. Specifically, the following core and variable classifiers are included: competency domain (e.g., psychology, biology), specific competencies (e.g., student will list all criteria for clinical diagnosis of anorexia nervosa), instructional design principle used (e.g., cognitive, psychomotor and affective domains), decay rate (e.g., 5 years), as well as level of proficiency (e.g., 75% mastery) and the validating agency (e.g., credentialing institution, prior learning assessment agency). An example of an upper­division high school psychology competency is depicted in the Figure 1. The proposed non­profit model would create an entity to provide development, implementation, and oversight of the classification index. While it is proposed that member institutions would have voting rights to add, modify and remove competencies from the index, an open and well­documented API would be provided allowing anyone to benefit from the index. This API will allow queries and provide a technical framework for future application and system development. For example, the goal definition capabilities of learning management systems could be streamlined by leveraging existing competencies, entities empowered to evaluate prior learning could ensure that such learning is assessed using agreed upon standards, and workforce development centers could more easily place qualified job seekers with employers. Furthermore, transcription systems could report competencies in addition to reporting completed coursework and earned credentials. Figure 1. Anatomy of the Competency Classification Index with sample psychology competency code shown in green. In summary, the practicality of competency­based education will improve for all stakeholders in the educational and workforce development ecosystem by systematically classifying and defining specific competencies through an agreed­upon coding scheme. Impact The impact of the CCI will be far­reaching and will influence numerous initiatives that educational institutions and technology providers are implementing to develop competency­based solutions and greater transparency around learning and credentials. The CCI would allow these organizations to develop ‘their solutions’ using a common competency classification schema. The impact will be measured qualitatively through use cases and input from member institutions and various stakeholders. Efficient & Cost Effective for Stakeholders Numerous efficiencies can be achieved through the development of a competency classification index that is aligned with all knowledge, skills, and attitudes a learner may achieve. For instance, the tabulation and review of transcripts using a common taxonomy for learning achieved at other post­secondary institutions would eliminate the time­consuming review of syllabi to determine transfer equivalencies. Transfer equivalencies could be determined by aligning the competencies achieved in all previous courses. This also provides an opportunity for learners to address any deficiencies through prior learning assessment or any other competency­recovery mechanisms. Student centered Students benefit because a coding system recognizes the personalized nature of learning and eliminates the ambiguity found in academic credential requirements (e.g., high school diplomas, college degrees) that currently serve as measures of learning. A coding system standardizes learning experiences without being prescriptive in delivery. Similarly, a coding system can help identify gaps in the availability of learning experience in underserved communities. Furthermore, a transparent and prescriptive classification schema enables students to select efficient learning pathways. Summary The development of an open and innovative classification schema facilitates the ability to assess competency achievements throughout the P­20 educational pipeline, adult workforce development programs, as well as competencies earned in the workforce and demonstrated through prior learning assessments. Furthermore, all classified competencies would be determined through open and highly collaborative processes and governance. The competency classification index aims to radically simplify the transparency, definition, and transfer of competencies. Resources Competency Classification Non­Profit Bylaws Link to Competency Classification Multimedia Item References Bidwell, A. (2014). Report: 1 in 10 Community College Transfers Lose Nearly All Course Credits. Retrieved from http://www.usnews.com/news/articles/2014/03/19/report­1­in­10­community­college­transfers­lo
se­nearly­all­course­credits Competency Model Clearinghouse. (2015). Retrieved from http://www.careeronestop.org/competencymodel/pyramid_definition.aspx Selingo, J. (2015, January 26). Why are so many college students failing to gain job skills before graduation? Retrieved from https://www.washingtonpost.com/news/grade­point/wp/2015/01/26/why­are­so­many­college­st
udents­failing­to­gain­job­skills­before­graduation/