Public Administration Curriculum-Based Big Data Policy-Analytic Epistemology: Symbolic IoT Action-Learning Solution Model

Public Administration Curriculum-Based Big Data Policy-Analytic Epistemology: Symbolic IoT Action-Learning Solution Model

Emmanuel N. A. Tetteh (Norwich University, USA & Action Learning, Action Research Association, USA)
Copyright: © 2019 |Pages: 22
DOI: 10.4018/978-1-5225-7432-3.ch024

Abstract

The equilibration that underscores the internet of things (IoT) and big data analytics (BDA) cannot be underestimated at the behest of real-life social challenges and significant policy data generated to redress the concerns of epistemic communities, such as political policy actors, stakeholders, and the citizenry. The cognitive balancing of new information gathered by BDA and assimilated across the IoT is at the crossroads of ascertaining how the growing increases of such BDA can be better managed to transition from the big data state of disequilibration to reach a more stable equilibrium of policy data usefulness. In the quest for explicating the equilibration of policy data usefulness, an account of the curriculum-based MPA policy analysis and analytics concentration program at Norwich University is described as a case example of big data policy-analytic epistemology. The case study offers a symbolic ideology of an IoT action-learning solution model as a recommendation for fostering the stable equilibration of policy data usefulness.
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Organization Background

This section provides an introductory viewpoint of the background on the case history of the organization underscoring the experiential learning context of the curriculum-based Master of Public Administration (MPA) Policy Analysis and Analytics (PAA) concentration program at Norwich University (NU). Since its inception in 1819, NU, founded by Captain Alden Partridge, a former United States Military Academy Superintendent, has remained well-committed to the philosophy of experiential learning for preparation of traditional-age and nontraditional-age students in a Corps of Cadets and as civilians to advance future societal leadership, service professionalism, and business industries (Norwich University, 2014a). Building upon the works of Dewey, Lewin, and Piaget, Kolb (1984) made a significant contribution to the experiential learning theorization model. According to Kolb, experiential learning fosters the creation of knowledge through critical thinking and persistent adaptation to community engagement, as can be attested to or derived by the process of concrete experience, and also modified by reflective learning, conceptual evaluation, and active investigation (Bergsteiner, Avery, & Neumann, 2010; Kolb, 1984).

By simplifying Kolb’s theorization, the experiential learning model has been conceptualized as “an experience or problem situation; a reflective phase in which the learner examines the experience and creates learning from his/her reflection; and an application phase in which the new knowledge or skills are applied to a new problem or situation” (National Institute of Food and Agriculture, 2017, p. 1). As a coeducational institution of experiential learning pedagogy and andragogy in Northfield, Vermont, as well as one of America’s six senior military institutions of higher learning and the initiation of the Reserve Officers’ Training Corps (ROTC), NU offers various traditional learning and distance-learning baccalaureate and graduate degree programs to approximately 3,500 students (Norwich University, 2015).

Recognizing its enormous contribution to the ROTC, along with its training of military officers and non-military learners for various careers in the business enterprise, government agencies, and military service, as well as for the pursuit of academic degrees, NU has evolved in many significant ways over its almost 200 years. In 2014, it began the Forging the Future initiative in preparation for its bicentennial celebration in 2019 (Norwich University, 2010, 2014a). This five-year campaign for the bicentennial celebration is geared toward fostering an increased level of innovative learning atmosphere through high-tech pedagogical and restructuring of top-notch facilities to contribute to the university’s vitality of service innovation to the nation (Norwich University, 2014a).

In keeping with the Forging the Future campaign initiatives and in alignment with its mission mandate, NU’s College of Graduate and Continuing Studies (CGCS) has resoundingly remained more committed to providing lifelong learners with dynamic experiential learning model. This dynamic experiential learning paradigm is structured on the balance between learners’ real-life challenges and the application of:

  • A collaborative action-learning model;

  • Action research modalities;

  • Knowledge and process management protocols;

  • Public service leadership via the traditional face-to-face teaching/learning model and the open and distance learning (ODL) framework of fostering pragmatic learning (Norwich University, 2014b).

Accredited by the New England Association of Schools and Colleges, the University’s Board of Trustees adopted its mission mandate as:

Key Terms in this Chapter

Heuristic Epistemology: The equilibration of coherent and consistent data-generated knowledge of the data triangulation structuring apperception, appraisal, and appropriation techniques that underscores the worldviews of the epistemic communities.

Disequilibration: The unbalancing of big data analytics across the IoT created by the shortcomings inherent in the divergence or discrepant cases of data, data outliers, or inaccurate observation or illogical reasoning.

Data Deontological Hermeneutics: The interpretation of data regarding the means by which the rightness or wrongness of data analytics might be conceived to implicate the sense-making process of big data analytics.

Data Teleological Hermeneutics: The alignment, fitness, or suitability of challenging the interpretation of data structuring big data analytics.

Epistemic Communities: A network of the ethos of political policy actors, stakeholders, the citizenry, and professionals with the data capacity of shared knowledge, policy-relevant knowledge, policy analytic data, policy coordination, policy innovation, principled belief systems, mutual expectations, collective action, and shared interpretations.

Stable Equilibration/Re-Equilibration: The rebalancing of big data analytics created by the elimination of shortcomings inherent in the divergence or discrepant cases of data, data outliers, or inaccurate observations or illogical reasoning to contribute to the IoT action-learning solution.

Political Ideology: The balancing of a coherent and consistent set of policy beliefs and techno-politic data interest that implicates public policy data epistemology.

Public Policy Data Epistemology: The problem resolution continuum of evolving big data analytics of complex situational needs and public policy issues of organization and citizenry across the power of the IoT ecosystem framework of the epistemic communities.

Equilibration: The balancing of big data analytics created by the initial stage of policy data analysis across the IoT.

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