"Excellence is never an accident. It is always the result of high intention, sincere effort, and intelligent execution; it represents the wise choice of many alternatives—choice, not chance, determines your destiny.”—Aristotle.
If Aristotle is correct, then we in academic advising can always use assistance to be as intentional as possible in our practice. Advising as teaching is a paradigm that has been advocated by many authors (Lowenstein, 2005; Hemwall & Trachte, 1999, 2003), but intentionally identifying what students need to learn is critical. As Martin (2007) stated, “Learning objectives answer the question: what should students learn through academic advising?” Likewise, Steele (2014) argued for the intentional use of technologies as tools.
Tools are designed for specific uses. The best use of technologies is when their capabilities align with our advising goals. To help advisors achieve better student learning outcomes and improve program assessment, this article will use elements of Steele’s model for intentional use of technology and combine these with elements of the curriculum development model called Understanding by Design. Integrated, these two models offer a conceptual way to reconsider how to organize learning outcomes and program assessment.
Intentionality is a critical component in the curriculum development model Understanding by Design (UbD) created by McTighe and Wiggins (2005, 2011). A synopsis of their UbD model contains three main stages:
Stage 1: Identify desired results by identifying what students should know, understand, and be able to do.
Stage 2: Determine assessment evidence by identifying how we will know if students have achieved the desired results.
Stage 3: Plan learning experiences and instruction that address transfer of learning, meaning making, and acquisition.
While McTighe and Wiggins’s focus has been on K–12 education, their model has much to offer college academic advisors using an advising as teaching approach, because their central idea is to align what we assess to what we planned to achieve for student learning.
While the UbD model offers more in-depth considerations and guidelines than what can be presented here, there are several key points of the UbD model that are helpful for our purpose. In its first key step, the UbD model offers insights about identifying desired results by identifying what students should know, understand, and be able to do. The model suggests that learning outcomes be evaluated in the context of what should be the key understandings of the outcome and what are the essential questions related to specific outcomes. For our purpose, as an example, an essential academic advising goal is engaging in self-assessment to develop an academic and career plan. An example of understandings and essential questions related to this goal is suggested in the graph below.
Satisfaction and success in educational and career endeavors are more likely when there is compatibility between one’s interests, abilities, and values and one’s college major and future work.
How can students discover the relationship between their self-knowledge and educational and career possibilities?
By determining what are the essential learning outcomes, verses those important or nice to know, we are forced to make value decision about what we believe is critical for our students to learn. For example, what is a more critical learning outcome? Having a student engage in a successful educational and career planning project, or showing up to a scheduled meeting on time? By determining the essential academic advising learning outcomes in this fashion, we can also stress the importance of better aligning them to our institutional mission and working with the collaborative campus community to achieve them (Campbell, 2008; Robbins & Zarges, 2011).
The second stage McTighe and Wiggins identify is determining assessment evidence. For our sake, let’s call this student learning evaluation to better align it with the nomenclature found within the field of academic advising. At this stage it is also important to consider which technology tools can best assist with capturing evaluation evidence of student learning. This decision should be guided by the levels of learning we seek.
McTighe and Wiggins (2012) present their own taxonomy of terms to describe understanding in the different levels of learning. In order of complexity, it begins at the simple stage of explaining and moves through levels of interpret, apply, demonstrate perspective, display empathy, and finally to have self-knowledge. For them, this last level is a meta-cognitive level of awareness where one uses productive habits of mind, developed through past learning, to reflect on the meaning of current learning and experiences. Those familiar with Bloom’s Taxonomies (Bloom, 1953; Anderson, et. al, 2001) can see parallels with McTighe and Wiggins’s taxonomy in terms of defining learning outcomes. Both taxonomies move from the simple to more complex functioning; while McTighe and Wiggins present one classification taxonomy, Bloom and his colleagues defined three taxonomy domains: cognitive, affective, and behavioral.
Stage three of McTighe and Wiggins’s approach is to plan learning experiences and instruction that address transfer of learning, meaning making, and acquisition. At this step, advisors can consider what activities might help achieve desired learning outcomes. Consider the example in step one: activities for self-assessment and exploration might include use of online self-assessment tools, interviewing students and faculty in specific academic programs, considering different career options that might be related to specific academic majors, and explaining why the academic and career direction students select aligns well with their personal and professional values. A quick scan of these suggested activities shows they include both virtual and traditional experiences. The advantages are maintained as long as the means of evaluation relies on the use of digital technologies. By doing so, advisors can capitalize on the key to McTighe and Wiggins’s (2012) approach that “encourages teachers and curriculum developers to first think like assessors before designing specific units and lessons” (p. 7).
The intentional use of technology model identifies three categories of technologies used in advising: service, engagement, and learning (Steele, 2014). The service category highlights those tools that provide institutional services through personalized student accounts such as student information systems and degree audits. The engagement category identifies tools to inform and build communities with students and others at the institution, such as social media, static websites, and many electronic communication tools. The learning category includes tools such as e-Portfolios, learning management systems, interactive video conferencing, and early alert systems. A key element in this category is including a means to digitally evaluate student learning through mastery of content, skills developed, a project produced, plan submitted, or demonstration of reflection on a topic or issue. While it seems self-evident that tools in the learning area can assist with achieving learning outcomes, the integration of tools found in all three areas can lead to the creation of a more robust and enriched digital learning environment that focuses on student learning and program assessment.
Regardless of the taxonomy used, the central point is that different types of evaluation tools are better at evaluating different levels of student learning. Using the general learning goal presented earlier, the chart below suggests relationships between: 1) assessment evidence, 2) types of technologies identified in Steele’s model under learning, and 3) specific digital assessment tools used in specific technologies.
Use of a rubric to assess quality of students’ response in a written paper submitted to the LMS Drop box
Use of rubrics to assess 1) relationship between students’ self-assessments and selection of major and vocational direction, 2) quality of presentation, and 3) quality of the explanation of choices
The progression illustrated in this table shows that technology tools for evaluating students can be differentiated in terms of their effectiveness at different levels of student learning. Technology tools should be aligned with student-learning outcomes beginning with simple tools found in most Learning Management Systems (e.g., quizzes, fill-in-the-blank, matching, and short answer responses).
Evaluation tools at this level tend to focus on the lower levels of learning taxonomies: information recall, comparing, or explaining. More complex learning outcomes require more sophisticated technology tools including tools that allow for writing papers demonstrating careful reflection on the exploration process or submitting an academic and career exploration project in an e-Portfolio tool. These more sophisticated efforts often rely on use of rubrics for evaluating student learning (example). Evaluation tools at this level tend to focus on the higher levels of the learning taxonomies that seek demonstrations of meta-cognitive levels of awareness.
Such approaches to evaluate students are familiar to academic advisors in traditional learning settings. When advisors use appropriate technologies to help assess students learning, two critical advantages are evident: 1) content modules related to specific learning outcomes can easily be aligned to evaluation tools through a LMS or e-Portfolio and 2) results of student learning are digitally stored in relation to specific learning outcomes.
Technologies found in the learning component of Steele’s model are not the only technologies that can advance learning. For instance, degree audits are categorized under “services.” We can ask students to produce a degree audit as one measure of a student-learning outcome. More importantly, we can have students interpret what the degree audit means to them through a reflection paper submitted in the LMS.
Many of the technologies listed in the social category exist outside the institution’s student portal, what Steele referred to as crossing the “FERPA Line” where institutional security does not exist. Thus, advisors must use these technologies with caution. Still, we can suggest students use services like a LinkedIn site to connect with alumni from the institution who participate because they want to share their career experiences with currently enrolled students. Students could consider their interactions with these alumni in the context of their e-Portfolio project or a reflection paper, which would then be submitted through an LMS. Students who engage in these types of activities demonstrate higher order thinking through reflection.
The use of models from both McTighe and Wiggins and Steele can help advisors with program assessment in significant ways. One advantage is that identification of outcomes for advising can be classified as process/delivery outcomes (PDOs) for the advising program and student learning outcomes (SLOs) (Campbell, 2008; Robins & Zarges, 2011). Creating SLOs is necessary in stage one of the UbD model. When technology is used to evaluate the student learning outcomes, students’ results are in a digital format that can be easily aggregated. Thus, advisors can more efficiently determine how well a program’s activities helped students achieve desired SLOs. Similarly, another benefit is that because an integrated approach encourages advising programs to use technology for content and service delivery, students are using technologies in different ways. When students use technologies located within the institution’s student portal (service and learning technologies), it becomes easier to track student use. When students use a degree audit report to help explain their choices for educational and career planning, their use of the degree audit produces PDOs. If they write a paper explaining why they chose a particular program, the paper can be an activity associated with an SLO. Rich sources for program assessment are created by aggregating both SLO and PDO data in this manner.
Curriculum planning and the intentional use of technology help advisors evaluate essential learning and prove how advisors increase student learning while aiding program assessment.
Hemwall, M. K., & Trachte, K. C. (2003). Academic advising and a learning paradigm. In M.K. Hemwall & K. C. Trachte (Eds.), Advising and learning: Academic advising from the perspective of small colleges and universities (NACADA Monograph No. 8, pp. 13–20). Manhattan, KS: National Academic Advising Association.
Robbins, R., & Zarges, K.M. (2011). Assessment of academic advising: A summary of the process. Retrieved from NACADA Clearinghouse of Academic Advising Resources website:
Steele, G. (2014). Intentional use of technology for academic advising NACADA Clearinghouse Resource website:
Wiggins, G., & McTighe, J. (2011). The understanding by design guide to creating high-quality units. Alexandria, VA: ASCD
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