Analysis of Student Learning Outcomes in Data Structures and Algorithms
This study attempts to quantify SLOs of a graduate level foundation course named Algorithms and Data Structures and shows better success because of appropriate course modality, teaching pedagogy and engagement among many others.
Abstract
Student learning outcomes (SLOs) are the specified knowledge, skills, and abilities that students are expected to achieve by the end of a course or program. Student learning outcomes describe what educators expect students to know and be able to do. SLO's specify an action of a student that must be observable, measurable and demonstrable. On the other hand, grades are objectives measure. This study attempts to quantify SLOs of a graduate level foundation course named Algorithms and Data Structures. Three key aspects of the course are defined to address learning outcomes: a) Algorithm Design Skill, b) Data Structure Selection and Design Skill and c) Algorithm Analysis Ability. For each aspect five achievement classes are specified in order to observe and quantify their knowledge, skills and ability. According to self-assessment, 86% students can a) determine the correct time and space complexities in an elegant and effective way, and b) correctly figure out time and space complexities of the designed algorithm but more elegant analysis exists. The study also shows 86% students can a) design elegant and robust algorithms and b) the designed algorithm is correct and efficient in most cases but it does not scale well in extreme cases. And finally, 79.7% students mentioned that a) data structure is well chosen (i.e., efficient) and well justified, and b) data structure is appropriate, but operations are not described sufficiently clearly. This clearly shows better success because of appropriate course modality, teaching pedagogy and engagement among many others.