Board # 80 : Measuring the Impact of Adaptive Learning Modules in Digital Logic Courses

This paper presents the current status of a research project investigating the use of a novel web-based adaptive learning system to improve student mastery of digital logic concepts while considering the demographics of the individual student. Adaptive learning is a pedagogical approach that dynamically alters the difficulty of content based on an ongoing assessment of the student’s capability. This technique is becoming more popular with the advancement of webbased learning solutions and increased student enrollment. Using this type of e-learning environment has the potential to address background deficiencies of students who lack the necessary prerequisite skills coming out of high school. This three-year project is currently in its second year through funding from the National Science Foundation’s Improving Undergraduate STEM Education (IUSE). During the first year of this project our team developed a set of course materials and assessment instruments for the area of digital logic. This is content found in all accredited undergraduate electrical and computer engineering curriculums. In year two, our team used the material in a variety of course delivery formats including live, online to on-campus students, online to off-campus students, and online to remote community college students. Data was collected on student performance while also tracking student demographics such as gender, ethnicity, GPA, credits earned, ACT scores, and transfer credits. The data was analyzed to determine if there were any learning outcomes that had significantly lower student performance overall, and if there were any specific student sub-groups that performed lower on any of the outcomes. In year three of the project our team is deploying adaptive learning modules on targeted outcomes to measure their impact. This paper will present the course materials developed during year one, the data and the baseline results collected during year two, and the initial results of the adaptive learning modules. This paper will benefit engineering educators teaching logic circuits/design and are interested in using an online learning environment to either supplement or replace in-class instruction. Introduction Engineering program enrollments have been increasing steadily for nearly a decade, and instructors are investigating ways to maintain or even improve the quality of the student learning experience in this challenging environment. Adding to the complexity is the wide range of preparedness students have when beginning college. E-learning environments offer one way to supplement face-to-face instruction; designed properly, e-learning can be scalable and can personalize instruction to address background deficiencies. An adaptive e-learning system is an exciting pedagogical tool that can provide individual instruction to students by dynamically altering the difficulty of content based on an ongoing assessment of the students’ capability. In its simplest form, an adaptive learning system consists of a bank of online quiz questions on a particular subject, each with an associated difficulty level. As students answer questions, the difficulty of the next question either increases or decreases based on the students’ response. In a more comprehensive form, additional targeted instruction can be provided if students answer questions incorrectly. Additionally, more thought-provoking material can be presented to students who consistently answer questions correctly, providing challenge to students when appropriate. Individualized, computer-based, adaptive learning has been shown to be nearly as effective as a live instructor guiding the student through the material when implemented carefully [1,2]. Most course management systems (i.e., Desire2Learn, Moodle, Blackboard) support question banks that are dynamically assigned based on difficulty and continual student assessment. Thus, the infrastructure to exploit adaptive learning systems for personalized instruction has greatly improved over the last decade. Part 1 – Creating Curriculum Materials to Measure a Baseline of Understanding The first portion of this project was to define the overall learning objectives and specific learning outcomes for students in introductory digital logic courses. The following figure shows the learning objectives and outcomes defined for this project. For each outcome, the associated learning category within Bloom’s Taxonomy. The taxonomy becomes important when designing the assessment tools to measure each learning outcome as they guide what information is actually being assessed. 6