Abstract:

Students majoring in computer science (CS) come in at varying levels of exposure. While trying to adapt and provide appropriate intervention to each student is ideal, it has proven to be a daunting task for the instructors. In order to properly prepare students to succeed in a corporate environment or in an advanced degree program, students need to be actively engaged in activities/interventions that address (adapt to) his/her academic needs. The Structured Adaptive Individualized Learning System (SAILS) is proposed to address the challenges of the under-prepared undergraduate computer science students. Given that students learn differently and at varying rates, SAILS will utilize the Felder-Soloman Learning Style Index (FLSI) in conjunction with Bloom’s Revised Taxonomy to ensure a robust and well-rounded student experience. To help improve self-efficacy and self-regulation, SAILS will provide a dashboard for students to get a visual representation of their performance using graphs, goal setting maps, goal achievement progressions, etc. SAILS research will use a quasi-experimental design and implementation, that will provide the content and interventions that will help close the experience gaps so students can unequivocally meet learning outcomes by the end of the course.

The number of computer science jobs is growing significantly faster than the average (non-computer science) job growth. Even with this positive employment outlook, the number of incoming computer scientists is not matching the projected job increase [1] [2]. Reassuring students, who enter computer science with a lack of prior exposure to the field, is important in their retention. It is also important not to continually challenge those students who are keeping pace or advanced. Therefore providing students with material that is on their cognitive level is vital to the engagement, success, and retention of the student.