John M. Quick

Computer-aided instruction for introductory linear circuit analysis

A step-based tutoring system for linear circuit analysis is being developed with the capabilities to automatically generate circuit problems with specified characteristics, including randomly generated topologies and element values. The system further generates fully-worked, error-free solutions using the methods typically taught in such classes, and accepts a rich variety of student input such as equations, matrix equations, numerical and multiple-choice answers, re-drawn circuit diagrams, and sketches of waveforms. A randomized, controlled study was conducted using paid student volunteers to compare the effectiveness of two of our tutorials in comparison to working conventional textbook-based problems. The average learning gain was only 3/100 points for the textbook users, but 29/100 points, about 10 times higher, for the tutorial users. The effect size on the post-test scores was 1.21 pooled standard deviations (Cohen d-value) and was statistically significant. A motivational survey administered to these students yielded a 0.53 point higher rating for the software than for the textbook (on a 1-5 scale). The system is being used in Spring 2013 by over 340 students in EEE 202 at Arizona State and two community colleges. About 99% of these students rated the system as “very helpful” or “somewhat helpful”.

mLearning: An Embodied Perspective

Rapid advances in mobile hardware and software have fueled the emergence of mobile learning (mLearning) as an area of interest within educational research. In spite of abundant excitement and discussion surrounding mLearning, certain challenges remain unresolved. To date, it seems the field is most enamored with categorizing hardware capabilities and replicating conventional learning content on portable devices. However, it would be beneficial to consider how mLearning can augment learners’ education in unique ways. Prior definitions of mLearning fall short in articulating why mobility is beneficial for learning and how mobile learning is not simply a reformulation of existing eLearning methods. A new perspective is offered to establish mLearning as a unique type of learning. Evidence from the field of embodied cognition that points to the potential affordances of mLearning is provided and future directions are discussed.