The Doer Effect states that completing more active learning activities, like practice questions, is more strongly related to positive learning outcomes than passive learning activities, like reading, watching, or listening to course materials. Although broad, most evidence has emerged from practice with tutoring systems in Western, Industrialized, Rich, Educated, and Democratic (WEIRD) populations in North America and Europe. Does the Doer Effect generalize beyond WEIRD populations, where learners may practice in remote locales through different technologies? Through learning analytics, we provide evidence from N = 234 Ugandan students answering multiple-choice questions via phones and listening to lectures via community radio. Our findings support the hypothesis that active learning is more associated with learning outcomes than passive learning. We find this relationship is weaker for learners with higher prior educational attainment. Our findings motivate further study of the Doer Effect in diverse populations. We offer considerations for future research in designing and evaluating contextually relevant active and passive learning opportunities including leveraging familiar technology, increasing the number of practice opportunities, and aligning multiple data sources.

Despite the best intentions to support equity with educational technologies, they often lead to a “rich get richer” effect, in which communities of more advantaged learners gain greater benefit from these solutions. Effective design of these technologies necessitates a deeper understanding of learners in understudied contexts and their motivations to pursue an education. Consequently, we studied a 15-week remote course launched in 2021 with 17,896 learners that provided engineering education through a radio and phone-based system aimed for use in rural settings within Northern Uganda. We address shifts in learners’ motivations for course participation and investigate the impact of demographic features and motivations of students on persistence and performance. We found significant increases in student motivation to learn more about and pursue STEM. Importantly, the course was most successful for learners in demographics who typically experience fewer educational opportunities, showing promise for such technologies to close opportunity gaps.

Vector quantization is a popular approach to image compression as it allows images to be coded at less than one bit per pixel. This paper presents a modified fuzzy competitive learning algorithm and applies it to image data vector quantization. The proposed algorithm overcomes the neuron underutilization problem by applying both fuzzy learning and distortion equalization to the competitive learning algorithm. Experimental results on real image data shows that this approach produces a higher quality codebook than applying fuzzy learning or distortion equalization to the competitive learning algorithm individually.