OBACIS: Outcome Based Analytics and Continuous Improvement System

Mohamed A. Ismail


In this paper, an integrated system for outcome-based assessment and continuous improvement is presented. The system is designed and implemented as a suite of three integrated Apps: An Excel-App for creating Auto Grading Sheets (AGSs); a Web-App for building assessment
trees, updating server database(s), uploading associated documents, and conducting surveys; and a Win-App for program-wide and faculty-wide OBA data compilation, performance analysis, and data-informed continuous
improvement. The proposed system adopts a bottom-up approach for building assessment trees that define the structure and the smart logic embedded in AGSs. Some course assessment activities, possibly all, are mapped to graduate attributes, more precisely indicators, and course learning outcomes. The proposed system analyzes the collected data
from three different views: 1) Categorical Analysis view (CAs), 2) Learning Outcomes Analysis view (LOAs), and 3) Graduate Attributes Analysis (GAAs) view. The paper presents some principles related to the proposed system, demonstrates its multiple user interfaces, and digs more into
OBA analytics and its proposed closed-loop continues improvement process. The objective of the proposed system and its underlying framework is to set new grounds for the accreditation process by making it more appealing, more economical, and more fruitful for all involved stakeholders.

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