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The purpose of this research is to examine the benefits and limitations of the implementation of novel digital academic advising systems based on the principles of authentic outcome-based education (OBE) using automated collection and reporting processes for Accreditation Board for Engineering and Technology (ABET) student outcomes data for effective developmental advising. We examine digital developmental advising models of undergraduate engineering programs in two universities that employ customized features of the web-based software EvalTools® 6.0, including an advising module based on assessment methodology incorporating the faculty course assessment report, performance indicators, and hybrid rubrics classified according to the affective, cognitive, and psychomotor domains of Bloom’s learning model. A case study approach over a six-year period is adopted for this research. The two case studies present results of samples of developmental advising activity employing sequential explanatory mixed methods models using a combination of quantitative and qualitative analyses of (a) detailed students’ outcomes and performance indicator information and (b) self-evaluation of their professional development and lifelong learning skills. The findings of this study show that digital advising systems employing the faculty course assessment report using performance indicators and hybrid rubrics can provide comprehensive and realistic outcome data to help both developmental advisors and students easily identify the specific cause of performance failures, implement practical recommendations for remedial actions, and track improvements. Inherent strong skills can also be identified in academically weak students by observing patterns or trends of relatively better-performing outcomes to reinforce their natural affinity for learning specialized competencies to help them pursue related and successful career paths.
Hussain et al. (Mon,) studied this question.