Slott-Student Learning Outcomes Tracing Tool

Αρετή (Arete) Journal of Excellence in Global Leadership | Vol. 1 No. 1 | 2022

general education courses, or courses that map outcomes to a cultural ISLO are low. With a large number of courses offered (588), each with an average 8-10 course outcomes (~5,880 outcomes) the sheer amount of mapping data is overwhelming and difficult to analyze through conventional means. These approximately ~5,880 outcomes are collected from the course syllabi under Course Student Learning Outcomes (CSLO) and will be utilized to generate the SLOTT, to analyze the data more easily. Better Assessment Needs Better Tools Many educational institutions have used tracing tools and programs to filter and visualize large amounts of mapped/aligned learning outcome data. For example, Santa Clara University used concept and clustering mapping to outline and develop the framework for their library student learning outcomes (Branch, 2019). The concept mapping tool allowed for the University to come up with a wide number of student learning outcomes (Branch, 2019). Then the library used the tool of cluster mapping to narrow the number of outcomes and get rid of duplicated outcomes. This allowed the library to come up with their program student learning outcomes (PSLO) (Branch, 2019). At the University of Piraeus in Greece, they developed a tool (web-based) that uses Liferay, a Java-based component and open-source software, for their faculty to be able to map course outcomes to student outcomes. This program housed the data at the course level and was able to aggregate it to the student level (Ibrahim, 2015). The web tool was successfully implemented from their assessment committee to the whole faculty. Using this Liferay tool, the assessment committee was able to easily observe data gaps and action plans were developed when the benchmarks were not being met. However, challenges of the open-source Liferay software is the need for a trained Java programmer to utilize, which for other institutions could equate to hiring a new position and additional costs associated with the new position. The University of North Carolina at Chapel Hill built two methods to map and analyze the efficiency of their co-curricular outcomes. Using Extracurricular Involvement Inventory (EII) and heat maps the authors of the article were able to determine that most of the outcomes mapped to either communication or ethical skills. While the authors did not specify how they were going to address the gaps in the other outcomes, the tools used to generate the mapping data of their outcomes was extremely successful (Zeeman, 2019). The University of Canberra in Australia used frequency surveys to view gaps in how often their outcomes were measured (Lee, 2019), based on a value system of 1 through 4 on how often an outcome was measured. The mean and standard deviations of these values were then reviewed, and gaps in the measurement of outcomes were identified. The frequency surveys did have an element of subjectivity and further standardization of the 1 through 4 values would create more meaningful data. Creating tools to easily visualize data is not a novel concept but creating an easily utilized and modifiable tool presents a challenge.

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