Rethinking How We Design Programs

The landscape of higher education continues to change causing us to re-think the way we offer programs. Redesigning programs by listening to students pushes us to make radical changes. This chapter shows how the authors changed the content and delivery model by constantly reviewing student input on how we offer an online, competency-based alternative teacher certification program. They created annual measures that define program success, reviewed lead metrics to gain insight on areas working and needing improvements, and made ongoing changes to design and offer the program after listening to students' needs and desires. The program changes included continuous daily enrollment, changes in the instructor model to support student progression, an advising model focused on supporting individual student success throughout the program, and enhanced mentor support for fieldwork. The goal is to offer credentialing programs in different ways that prioritize accessibility, affordability, and applied field-based opportunities.

A Cognitive Knowledge-based Model for an Academic Adaptive e-Advising System

Aim/Purpose: This study describes a conceptual model, based on the principles of concept algebra that can provide intelligent academic advice using adaptive, knowledge-based feedback. The proposed model advises students based on their traits and academic history. The system aims to deliver adaptive advice to students using historical data from previous and current students. This data-driven approach utilizes a cognitive knowledge-based (CKB) model to update the weights (values that indicate the strength of relationships between concepts) that exist between student’s performances and recommended courses. Background: A research study conducted at the Public Authority for Applied Education and Training (PAAET), a higher education institution in Kuwait, indicates that students’ have positive perceptions of the e-Advising system. Most students believe that PAAET’s e-Advising system is effective because it allows them to check their academic status, provides a clear vision of their academic timeline, and is a convenient, user-friendly, and attractive online service. Student advising can be a tedious element of academic life but is necessary to fill the gap between student performance and degree requirements. Higher education institutions have prioritized assisting undecided students with career decisions for decades. An important feature of e-Advising systems is personalized feedback, where tailored advice is provided based on students' characteristics and other external parameters. Previous e-Advising systems provide students with advice without taking into consideration their different attributes and goals. Methodology: This research describes a model for an e-Advising system that enables students to select courses recommended based on their personalities and academic performance. Three algorithms are used to provide students with adaptive course selection advice: the knowledge elicitation algorithm that represents students' personalities and academic information, the knowledge bonding algorithm that combines related concepts or ideas within the knowledge base, and the adaptive e-Advising model that recommends relevant courses. The knowledge elicitation algorithm acquires student and academic characteristics from data provided, while the knowledge bonding algorithm fuses the newly acquired features with existing information in the database. The adaptive e-Advising algorithm provides recommended courses to students based on existing cognitive knowledge to overcome the issues associated with traditional knowledge representation methods. Contribution: The design and implementation of an adaptive e-Advising system are challenging because it relies on both academic and student traits. A model that incorporates the conceptual interaction between the various academic and student-specific components is needed to manage these challenges. While other e-Advising systems provide students with general advice, these earlier models are too rudimentary to take student characteristics (e.g., knowledge level, learning style, performance, demographics) into consideration. For the online systems that have replaced face-to-face academic advising to be effective, they need to take into consideration the dynamic nature of contemporary students and academic settings. Findings: The proposed algorithms can accommodate a highly diverse student body by providing information tailored to each student. The academic and student elements are represented as an Object-Attribute-Relationship (OAR) model. Recommendations for Practitioners: The model proposed here provides insight into the potential relationships between students’ characteristics and their academic standing. Furthermore, this novel e-Advising system provides large quantities of data and a platform through which to query students, which should enable developing more effective, knowledge-based approaches to academic advising. Recommendation for Researchers: The proposed model provides researches with a framework to incorporate various academic and student characteristics to determine the optimal advisory factors that affect a student’s performance. Impact on Society: The proposed model will benefit e-Advising system developers in implementing updateable algorithms that can be tested and improved to provide adaptive advice to students. The proposed approach can provide new insight to advisors on possible relationships between student’s characteristics and current academic settings. Thus, providing a means to develop new curriculums and approaches to learning. Future Research: In future studies, the proposed algorithms will be implemented, and the adaptive e-Advising model will be tested on real-world data and then further improved to cater to specific academic settings. The proposed model will benefit e-Advising system developers in implementing updateable algorithms that can be tested and improved to provide adaptive advisory to students. The approach proposed can provide new insight to advisors on possible relationships between student’s characteristics and current academic settings. Thus, providing a means to develop new curriculums and approaches to course recommendation.

Relationship-Centered Advising in a Medical School Learning Community

Background: Medical schools are required to have formal advising structures; however, there are limited data on how to optimally meet that mandate. Learning communities (LC), with their emphasis on longitudinal relationships, offer a unique scaffold for advising. Program description: The Johns Hopkins School of Medicine (JHSOM) LC focuses on curricular and extracurricular longitudinal connections between students and advisors. A core component of the LC is a relationship-centered advising (RCA) model drawing from best practices in physician–patient relationships, life coaching, and social contract theories. The key elements of the model include dyadic and small group advising, while the LC structure allows for faculty development in these domains. Relationship-centered advising approaches the collaborative advising work between students and advisors through explicit valuing of personal experiences, mutual respect, and earned trust. Framing the advising relationship in this way allows it to grow with the student along their medical school journey. Program evaluation & results: Student and faculty satisfaction with this model is high. Data from annual, anonymous student evaluations consistently indicate high degree of trust in and satisfaction from these relationships. Discussion: Relationship-centered advising aims to create a relationally anchored platform on which students can develop their personal and professional identities. This LC-based advising model is adaptable across schools regardless of structure and resources.

Boosting Retention Through a Foundation of Academic Advising

While there is no direct causal link between academic advising and increased student persistence, the role of the academic advisor can be key to an institution's success. This chapter examines one university's approach to redesigning the academic advising model from the ground up and committing to a philosophy of continuous improvement in academic advising, retention and student success. A decade in the making, the tools and approaches created through this process now play an important part in the institution's success and can be a road-map for other institutions to follow as they aspire to revise and improve their academic advising models and to improve student success.