The recent pandemic has emphasized the need for developing more effective pedagogical practices in online education. While activities that require more deep connection of concepts can enhance overall learning versus mere rote rehearsal, are conceptual activities also viable options for inclusion in online coursework, given the asynchronous and flexible nature of online learning? To answer this question, students in 2 online courses either completed concept-mapping exercises, or control exercises (i.e., definitions of concepts). Results indicate that while initially concept-mapping is no better than memorization, as experience with mapping increases, a growth in learning emerges later in the course. Students also rate the educational utility and enjoyablity of conceptual activities similarly to control exercises, suggesting that students do not perceive these conceptual activities more negatively. As such, concept-mapping does have a utility for online learning, but there is a necessary a warm-up period before benefits are realized.

Text skimming is a common reading behavior that occurs when readers scan text at a faster than normal rate to attempt to form understanding when not able to read at normal speed. Research has suggested that reading time varies across a skimmed text, guided by attention and comprehension goals. However, do individual differences in the ability to manage attention affect skimming? Are those better at managing attention (i.e., high working memory) also better at managing text skimming? Two experiments were conducted where participants who varied in WMC were asked to skim an unfamiliar expository text, both on a computer, and while being eyetracked. In both experiments, while participants did spend more time reading earlier portions of the text, this did interact with WMC. Those higher in WMC balanced their reading efforts more equally across the entire text, suggesting that text skimming behavior is sensitive to differences in WMC.

Motivation

We have created EcoPLOT (parameterized linkage of omics-driven technologies), a web-app for the dynamic, interactive analysis of biogeochemical datasets that combines state-of-the-art analysis tools to statistically and graphically explore environmental, geochemical and microbiome datasets. Using the iterative random forest, a machine learning algorithm, EcoPLOT allows for the de novo discovery of drivers which exhibit significant impact on plant, microbial or soil dynamics.

Availability and implementation

EcoPLOT is built entirely within the R language. It can be accessed through any system where R is installed, including Windows, Mac and most Linux systems. EcoPLOT is free to use and can be accessed at https://github.com/cdsanchez18/EcoPLOT.