Working Together: Librarian and Student Collaboration Through Active Learning in a Library Eclassroom

Active learning strategies based on several learning theories were incorporated during instruction sessions for second year Biological Sciences students. The instructional strategies described in this paper are based primarily on sociocultural and collaborative learning theory, with the goal being to expand the relatively small body of literature currently available that discusses the application of these learning theories to library instruction. The learning strategies employed successfully involved students in the learning process ensuring that the experiences were appropriate and effective. The researchers found that, as a result of these strategies (e.g. teaching moments based on the emerging needs of students) students’ interest in learning information literacy was increased and students interacted with information given to them as well as with their peers. Collaboration between the Librarians, Co-op Student and Senior Lab Instructor helped to enhance the learning experience for students and also revealed new aspects of the active learning experiences. The primary learning objective, which was to increase the students’ information skills in the Biological Sciences, was realized. The advantages of active learning were realized by both instructors and students. Advantages for students attained during these sessions include having their diverse learning styles addressed; increased interaction with and retention of information; increased responsibility for their own learning; the opportunity to value not only the instructors, but also themselves and their peers as sources of authority and knowledge; improved problem solving abilities; increased interest and opportunities for critical thinking, as a result of the actively exchanging information in a group. The primary advantage enjoyed by the instructors was the opportunity to collaborate with colleagues to reduce the preparation required to create effective library instruction sessions. Opportunities for further research were also discovered, including the degree to which “social loafing” plays a role in collaborative, active learning.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.